diff --git a/.github/workflows/release-test.yml b/.github/workflows/release-test.yml new file mode 100644 index 0000000..2230618 --- /dev/null +++ b/.github/workflows/release-test.yml @@ -0,0 +1,28 @@ +name: Release to Test.PyPI +on: + push: + branches: + - '!main' + - '!master' +jobs: + build-and-publish: + runs-on: ubuntu-20.04 + steps: + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: '3.11' + - name: Install Poetry + run: pipx install poetry + - name: Install dependencies + run: poetry install --with dev,llm + - name: Build packages + run: poetry build + - name: Configure Poetry + run: | + poetry config repositories.testpypi https://test.pypi.org/legacy/ + poetry config pypi-token.testpypi ${{ secrets.TEST_PYPI_API_TOKEN }} + - name: Publish to test PyPI + if: ${{ github.event_name == 'push' }} + run: poetry publish -r testpypi \ No newline at end of file diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 4be4ca9..eb8b4b2 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -11,19 +11,20 @@ jobs: runs-on: ubuntu-20.04 steps: - uses: actions/checkout@v3 - - name: Install Poetry - run: pipx install poetry - name: Set up Python uses: actions/setup-python@v4 with: - python-version: 3.8 - cache: poetry + python-version: '3.11' + - name: Install Poetry + run: pipx install poetry - name: Verify versioning run: | [ "$(poetry version -s)" == "${GITHUB_REF#refs/tags/v}" ] - name: Install dependencies - run: poetry install + run: poetry install --with dev,llm - name: Run tests + env: + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} run: poetry run pytest - name: Build packages run: poetry build @@ -32,9 +33,9 @@ jobs: poetry config repositories.testpypi https://test.pypi.org/legacy/ poetry config pypi-token.testpypi ${{ secrets.TEST_PYPI_API_TOKEN }} poetry config pypi-token.pypi "${{ secrets.PYPI_API_TOKEN }}" - - name: Publish to test PyPI - if: ${{ github.event_name == 'push' }} - run: poetry publish -r testpypi + #- name: Publish to test PyPI + # if: ${{ github.event_name == 'push' }} + # run: poetry publish -r testpypi - name: Publish to PyPI if: ${{ github.event_name == 'release' }} run: poetry publish \ No newline at end of file diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 4c76036..7dcbf8e 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -11,20 +11,26 @@ jobs: strategy: matrix: os: [ 'ubuntu-20.04', 'windows-2019', 'macos-12' ] - python-version: [ '3.8', '3.9', '3.10', '3.11' ] + python-version: [ '3.9', '3.10', '3.11', '3.12' ] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v3 - - name: Install Poetry - run: pipx install poetry - - name: Set up Python + - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v4 with: python-version: ${{ matrix.python-version }} - cache: poetry + - name: Install Poetry + run: pipx install poetry + - name: Configure Poetry + run: | + poetry env use ${{ matrix.python-version }} + poetry --version + poetry env info - name: Install dependencies - run: poetry install + run: poetry install --with dev,llm - name: Run tests + env: + OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} run: poetry run pytest --cov=sigmaiq --cov-report term --cov-report xml:cov.xml -vv - name: Store coverage for badge if: ${{ runner.os == 'Linux' }} diff --git a/README.md b/README.md index 48ad708..83cdcff 100644 --- a/README.md +++ b/README.md @@ -5,11 +5,29 @@

SigmAIQ: pySigma Wrapper & Utils

- ![Tests](https://github.com/AttackIQ/SigmAIQ/actions/workflows/test.yml/badge.svg) ![Coverage Badge](https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/slincoln-aiq/f6d72f7ec2b300546a114fd80d371f7e/raw/slincoln-aiq-SigmAIQ.json) ![Status](https://img.shields.io/badge/Status-pre--release-orange) +# Table of Contents +- [Table of Contents](#table-of-contents) +- [Introduction](#introduction) +- [Project Status](#project-status) +- [LLM Support](#llm-support) +- [Installation \& Usage](#installation--usage) + - [Requirements](#requirements) + - [Installation](#installation) + - [Usage Quickstart](#usage-quickstart) + - [Usage Examples](#usage-examples) +- [Supported Options](#supported-options) + - [Backends](#backends-1) + - [Pipelines](#pipelines-1) +- [Contributing](#contributing) +- [License](#license) +- [Maintainers](#maintainers) + +# Introduction + SigmAIQ is a wrapper for [pySigma](https://github.com/SigmaHQ/pySigma) and pySigma backends & pipelines. It allows detection engineers to easily convert Sigma rules and rule collections to SIEM/product queries without having to worry about the overhead of ensuring the correct pipelines and output formats are used by each pySigma supported backend. @@ -19,18 +37,18 @@ source code. If you don't see a backend that's currently supported, please consi community by making it with this [pySigma Cookiecutter Template](https://github.com/SigmaHQ/cookiecutter-pySigma-backend) -In addition, SigmAIQ will contain pySigma related tools and script updates coming in the near future, including easy +In addition, SigmAIQ contains pySigma related tools and scripts, including easy Sigma rule searching, LLM support, an automatic rule creation from IOCs. This library is currently maintained by: * [Stephen Lincoln](https://github.com/slincoln-aiq) via [AttackIQ](https://github.com/AttackIQ) -# Disclaimer +# Project Status + +SigmAIQ is currently in pre-release status. It is a constant work-in-progress and bugs may be encountered. Please report any issues [here](https://github.com/AttackIQ/SigmAIQ/issues/new). -This library is currently in pre-release status; therefore, it is a constant work-in-progress and bugs may be -encountered. Please report any issues [here](https://github.com/AttackIQ/SigmAIQ/issues/new). -Feature requests are also always welcome! pySigma tools/utils are currently not in the pre-release version, +Feature requests are always welcome! pySigma tools/utils are currently not in the pre-release version, and will be added in future releases. # LLM Support @@ -38,6 +56,10 @@ For LLM usage, see the [LLM README](sigmaiq/llm/README.md) # Installation & Usage +## Requirements +- Python 3.9+ +- pip, pipenv, or poetry + ## Installation SigmAIQ can be installed with your favorite package manager: @@ -316,6 +338,8 @@ Output: 'title': 'Test Rule'}]}}, 'crowdstrike_splunk': {'crowdstrike': {'default': ['event_simpleName="ProcessRollup2" ' 'CommandLine="mimikatz.exe"']}}, + 'crowdstrike_logscale': {'crowdstrike': {'default': ['event_simpleName="ProcessRollup2" ' + 'CommandLine="mimikatz.exe"']}}, 'elasticsearch': {'ecs_windows': {'default': ['process.command_line:mimikatz.exe'], ... ``` @@ -326,94 +350,106 @@ Output: ### Available Backends -| Backend Option | Description | Associated Pipelines | Default Pipeline | -|----------------------|-----------------------------------------------------|------------------------------------------------------------------------------------|----------------------| -| carbonblack | Carbon Black EDR | carbonblack
carbonblack_enterprise | carbonblack | -| cortexxdr | Palo Alto Cortex XDR | cortexxdr | cortexxdr | -| crowdstrike_splunk | Crowdstrike Splunk Query | crowdstrike | crowdstrike | -| elasticsearch | Elastic Elasticsearch SIEM | ecs_windows
ecs_windows_old
ecs_zeek_beats
ecs_zeek_corelight
zeek_raw | ecs_windows | -| insightidr | Rapid7 InsightIDR SIEM | insightidr | insightidr | -| loki | Grafana Loki LogQL SIEM | loki_grafana_logfmt
loki_promtail_sysmon
loki_okta_system_log | loki_grafana_logfmt | -| microsoft365defender | Microsoft 365 Defender Advanced Hunting Query (KQL) | microsoft365defender | microsoft365defender | -| opensearch | OpenSearch Lucene | ecs_windows
ecs_windows_old
ecs_zeek_beats
ecs_zeek_corelight
zeek_raw | ecs_windows | -| qradar | IBM QRadar | qradar_fields
qradar_payload | qradar_fields | -| sentinelone | SentinelOne EDR | sentinelone | sentinelone | -| splunk | Splunk SIEM | splunk_windows
splunk_wineventlog
splunk_windows_sysmon_acc
splunk_cim_dm | splunk_windows | -| sigma | Original YAML/JSON Sigma Rule Output | sigma_default | sigma_default | -| stix | STIX 2.0 & STIX Shifter Queries | stix_2_0
stix_shifter | stix_2_0 | +| Backend Option | Description | Associated Pipelines | Default Pipeline | +|-----------------|-------------|----------------------|-------------------| +| carbonblack | Carbon Black EDR | carbonblack
carbonblack_enterprise | carbonblack | +| cortexxdr | Palo Alto Cortex XDR | cortexxdr | cortexxdr | +| crowdstrike_splunk | Crowdstrike FDR Splunk Query | crowdstrike_fdr | crowdstrike_fdr | +| crowdstrike_logscale | Crowdstrike Logscale Query | crowdstrike_falcon | crowdstrike_falcon | +| elasticsearch | Elastic Elasticsearch SIEM | ecs_windows
ecs_kubernetes
ecs_windows_old
ecs_zeek_beats
ecs_zeek_corelight
zeek_raw | ecs_windows | +| insightidr | Rapid7 InsightIDR SIEM | insightidr | insightidr | +| loki | Grafana Loki LogQL SIEM | loki_grafana_logfmt
loki_promtail_sysmon
loki_okta_system_log | loki_grafana_logfmt | +| microsoft_xdr | Microsoft XDR Advanced Hunting Query (KQL) (Defender, Office365, etc) | microsoft_xdr | microsoft_xdr | +| microsoft_sentinel_asim | Microsoft Sentinel ASIM Query (KQL) | sentinel_asim | sentinel_asim | +| microsoft_azure_monitor | Microsoft Azure Monitor Query (KQL) | azure_monitor | azure_monitor | +| netwitness | Netwitness Query | netwitness_windows | netwitness_windows | +| opensearch | OpenSearch Lucene | ecs_windows
ecs_windows_old
ecs_zeek_beats
ecs_zeek_corelight
zeek_raw | ecs_windows | +| qradar | IBM QRadar | qradar_fields
qradar_payload | qradar_fields | +| sentinelone | SentinelOne EDR | sentinelone | sentinelone | +| splunk | Splunk SIEM | splunk_windows
splunk_wineventlog
splunk_windows_sysmon_acc
splunk_cim_dm | splunk_windows | +| sigma | Original YAML/JSON Sigma Rule Output | sigma_default | sigma_default | +| stix | STIX 2.0 & STIX Shifter Queries | stix_2_0
stix_shifter | stix_2_0 | + ### Backend Output Formats -| Backend Option | Output Format Option | Description | -|----------------------|---------------------------|------------------------------------------------------------------------------------| -| carbonblack | default | Plain CarbonBlack queries | -| | json | CarbonBlack JSON query | -| cortexxdr | default | Plain CortexXDR XQL queries | -| | json | JSON Formatted CortexXDR XQL query | -| crowdstrike_splunk | default | Plain SPL queries | -| elasticsearch | default | Plain Elasticsearch Lucene queries | -| | kibana_ndjson | Kibana NDJSON import file with Lucene queries | -| | dsl_lucene | Elasticsearch query DSL with embedded Lucene queries | -| | siem_rule | Elasticsearch query DSL as SIEM Rules in JSON Format | -| | siem_rule_ndjson | Elasticsearch query DSL as SIEM Rules in NDJSON Format | -| insightidr | default | Simple log search query mode | -| | leql_advanced_search | Advanced Log Entry Query Language (LEQL) queries | -| | leql_detection_definition | LEQL format roughly matching the 'Rule Logic' tab in ABA detection rule definition | -| loki | default | Plain Loki queries | -| | ruler | Loki 'ruler' output format for generating alerts | -| microsoft365defender | default | KQL for Microsoft 365 Defender Advanced Hunting queries | -| opensearch | default | Plain OpenSearch Lucene queries | -| | dashboards_ndjson | OpenSearch Dashboards NDJSON import file with Lucene queries | -| | monitor_rule | OpenSearch monitor rule with embedded Lucene query | -| | dsl_lucene | OpenSearch query DSL with embedded Lucene queries | -| qradar | default | Plain QRadar queries | -| sentinelone | default | Plaintext | -| | json | JSON format | -| splunk | default | Plain SPL queries | -| | savedsearches | Plain SPL in a savedsearches.conf file | -| | data_model | Data model queries with tstats | -| | stanza | Enterprise Security savedsearches.conf stanza | -| sigma | default | Default output format | -| | yaml | Default Sigma Rule output format | -| | json | JSON style Sigma Rule Output | -| stix | default | Plain stix queries | +| Backend Option | Output Format Option | Description | +|-----------------|------------------------|-------------| +| carbonblack | default
json | Plain CarbonBlack queries
CarbonBlack JSON query | +| cortexxdr | default
json | Plain CortexXDR queries
json output format | +| crowdstrike_splunk | default | Plain SPL queries | +| crowdstrike_logscale | default | CrowdStrike LogScale queries | +| elasticsearch | default
kibana_ndjson
dsl_lucene
siem_rule
siem_rule_ndjson | Plain Elasticsearch Lucene queries
Kibana NDJSON import file with Lucene queries
Elasticsearch query DSL with embedded Lucene queries
Elasticsearch query DSL as SIEM Rules in JSON Format
Elasticsearch query DSL as SIEM Rules in NDJSON Format | +| insightidr | default
leql_advanced_search
leql_detection_definition | Simple log search query mode
Advanced Log Entry Query Language (LEQL) queries
LEQL format roughly matching the 'Rule Logic' tab in ABA detection rule definition | +| loki | default
ruler | Plain Loki queries
Loki 'ruler' output format for generating alerts | +| microsoft_xdr | default | Kusto Query Language search strings | +| microsoft_sentinel_asim | default | Kusto Query Language search strings | +| microsoft_azure_monitor | default | Kusto Query Language search strings | +| netwitness | default | Plain netwitness queries | +| opensearch | default
dashboards_ndjson
monitor_rule
dsl_lucene | Plain OpenSearch Lucene queries
OpenSearch Dashboards NDJSON import file with Lucene queries
OpenSearch monitor rule with embedded Lucene query
OpenSearch query DSL with embedded Lucene queries | +| qradar | default | Plain QRadar queries | +| sentinelone | default
json | Plaintext
JSON format | +| splunk | default
savedsearches
data_model
stanza | Plain SPL queries
Plain SPL in a savedsearches.conf file
Data model queries with tstats
Enterprise Security savedsearches.conf stanza | +| sigma | default
yaml
json | Default output format
Default Sigma Rule output format
JSON style Sigma Rule Output | +| stix | default | Plain stix queries | ## Pipelines ### Available Named Pipelines -| Pipeline Option | Description | -|---------------------------|----------------------------------------------------------------------------------------------------------------------------------------------| -| splunk_wineventlog | SigmAIQ Custom, combined windows_audit and splunk_windows pipelines to convert Sysmon fields to Windows Event Log fields for Splunk searches | -| carbonblack | Uses Carbon Black EDR field mappings | -| carbonblack_enterprise | Uses Carbon Black Enterprise EDR field mappings | -| cortexxdr | Cortex XDR field mappings | -| crowdstrike | Crowdstrike FDR Splunk Mappings | -| ecs_windows | ECS mapping for Windows event logs ingested with Winlogbeat | -| ecs_windows_old | ECS mapping for Windows event logs ingested with Winlogbeat <= 6.x | -| ecs_zeek_beats | Zeek ECS mapping from Elastic | -| ecs_zeek_corelight | Zeek ECS mapping from Corelight | -| zeek_raw | Zeek raw JSON log field naming | -| insightidr | InsightIDR Log Entry Query Language (LEQL) Transformations | -| loki_grafana_logfmt | Converts field names to logfmt labels used by Grafana | -| loki_promtail_sysmon | Parse and adjust field names for Windows sysmon data produced by promtail | -| loki_okta_system_log | Parse the Okta System Log event json, adjusting field-names appropriately | -| microsoft365defender | Mappings for Sysmon -> Advanced Hunting Query Table Schema | -| qradar_fields | Supports only the Sigma fields in the Field Mapping | -| qradar_payload | Uses UTF8(payload) instead of fields unsupported by the Field Mapping. | -| sigma_default | Empty ProcessingPipeline placeholder | -| sentinelone | Mappings for SentinelOne Deep Visibility Queries | -| splunk_windows | Splunk Query, Windows Mappings | -| splunk_windows_sysmon_acc | Splunk Query, Sysmon Mappings | -| splunk_cim_dm | Splunk Datamodel Field Mappings | -| stix_2_0 | STIX 2.0 Mappings | -| stix_shifter | STIX Shifter Mappings | -| windows_sysmon | Sysmon for Windows | -| windows_audit | Windows Event Logs | -| windows_logsource | Windows Logs, General | - -# TODO - -- readthedocs docs -- Add tooling for LLM, search, and IOC Rule Creation -- Clean up codebase \ No newline at end of file +| Pipeline Option | Description | Display Name | +|------------------|-------------|---------------| +| splunk_wineventlog | SigmAIQ Custom combined windows_audit and splunk_windows pipelines to convert Sysmon fields to Windows Event Log fields for Splunk searches | Splunk WinEventLog | +| carbonblack | Uses Carbon Black EDR field mappings | CB | +| cortexxdr | Uses Palo Alto Cortex XDR field mappings | Palo Alto Cortex XDR | +| carbonblack_enterprise | Uses Carbon Black Enterprise EDR field mappings | CB | +| crowdstrike_fdr | Crowdstrike FDR Splunk Mappings | CrowdStrike FDR SPL | +| crowdstrike_falcon | Crowdstrike Falcon Logscale Mappings | CrowdStrike Falcon Logscale | +| ecs_kubernetes | Elastic Common Schema (ECS) Kubernetes audit log mappings | ECS Kubernetes | +| ecs_windows | Elastic Common Schema (ECS) Windows log mappings from Winlogbeat from version 7 | ECS Winlogbeat | +| ecs_windows_old | Elastic Common Schema (ECS) Windows log mappings from Winlogbeat up to version 6 | ESC Winlogbeat (<= v6.x) | +| ecs_zeek_beats | Elastic Common Schema (ECS) for Zeek using filebeat >= 7.6.1 | ECS Zeek (Elastic) | +| ecs_zeek_corelight | Elastic Common Schema (ECS) mapping from Corelight | ESC Zeek (Corelight) | +| zeek_raw | Zeek raw JSON field naming | Zeek Raw JSON | +| insightidr | InsightIDR Log Entry Query Language (LEQL) Transformations | InsightIDR LEQL | +| loki_grafana_logfmt | Converts field names to logfmt labels used by Grafana | Logfmt Labels | +| loki_promtail_sysmon | Parse and adjust field names for Windows sysmon data produced by promtail | WinSysmon Promtail | +| loki_okta_system_log | Parse the Okta System Log event json, adjusting field-names appropriately | Okta System Event | +| microsoft_xdr | Mappings for Sysmon -> XDR Advanced Hunting Query Table Schema | Microsoft XDR KustoQL | +| sentinel_asim | Mappings for Sysmon -> Sentinel ASIM Query Table Schema | Sentinel ASIM KustoQL | +| azure_monitor | Mappings for Sysmon -> Azure Monitor Query Table Schema | Azure Monitor KustoQL | +| netwitness_windows | Netwitness Windows log mappings | Netwitness Windows | +| qradar_fields | Supports only the Sigma fields in the Field Mapping | Sigma Fields | +| qradar_payload | Uses UTF8(payload) instead of fields unsupported by the Field Mapping. | UTF8(payload) (Non-Sigma Fields) | +| sigma_default | Empty ProcessingPipeline placeholder | Sigma | +| sentinelone | Mappings for SentinelOne Deep Visibility Queries | SentinelOne Deep Visibility | +| splunk_windows | Splunk Query, Windows Mappings | Splunk Query (Windows) | +| splunk_windows_sysmon_acc | Splunk Windows Sysmon search acceleration keywords | Splunk Query (Sysmon) | +| splunk_cim_dm | Splunk Datamodel Field Mappings | Splunk Datamodel Query | +| stix_2_0 | STIX 2.0 Mappings | STIX 2.0 | +| stix_shifter | STIX Shifter Mappings | STIX Shifter | +| windows_sysmon | Sysmon for Windows | Sysmon | +| windows_audit | Windows Event Logs | Windows Event Logs | +| windows_logsource | Windows Logs, General | Windows Logs, General | + +# Contributing + +We welcome contributions to SigmAIQ! Here's how you can contribute: + +1. Fork the repository +2. Create a new branch for your feature or bug fix +3. Make your changes and commit them with a clear commit message +4. Push your changes to your fork +5. Submit a pull request to the main repository + +Please ensure your code adheres to the project's coding standards and includes appropriate tests. + +# License + +This project is licensed under the [LGPL License](LICENSE). + +# Maintainers + +This library is currently maintained by: + +* [Stephen Lincoln](https://github.com/slincoln-aiq) via [AttackIQ](https://github.com/AttackIQ) diff --git a/examples/custom_field_mappings.py b/examples/custom_field_mappings.py index 61e49ed..d2f29b7 100644 --- a/examples/custom_field_mappings.py +++ b/examples/custom_field_mappings.py @@ -1,49 +1,47 @@ -# %% This example shows how to use the SigmAIQ pySigma wrapper to provide custom field mappings for a backend -# %% This will allow you to translate specific field names to custom field names during rule translation - -# %% Import SigmAIQ -from sigmaiq import SigmAIQBackend, SigmAIQPipeline - -# %% Import pprint for pretty printing, and copy for copying rules -from pprint import pprint -from copy import copy - -# %% A basic Sigma Rule in YAML str to convert to a query. -# %% SigmAIQ also accepts a rule in JSON/Dict format, SigmaRule objects, and SigmaCollection objects - -sigma_rule = """ -title: whoami Command -description: Detects a basic whoami commandline execution -logsource: - product: windows - category: process_creation -detection: - selection1: - - CommandLine|contains: 'whoami.exe' - condition: selection1 -""" - -# %% Create SigmAIQ backend translate the rule to a Microsoft 365 Defender query -sigmaiq_backend = SigmAIQBackend(backend="splunk").create_backend() -query = sigmaiq_backend.translate(copy(sigma_rule)) # Returns List of queries - -print("\nM365Defender Query: ", end="\n\n") -pprint(query[0]) -print("\n-------------------") - -# %% Create custom field mappings -# %% This will map the CommandLine field to a custom field named "CustomCommandLine" -custom_field_mappings = {"CommandLine": "CustomCommandLine"} -my_custom_pipeline = SigmAIQPipeline.from_fieldmap(custom_field_mappings, priority=0).create_pipeline() - -# %% Create SigmAIQ backend translate the rule to a Microsoft 365 Defender query with our custom field mappings -sigmaiq_backend = SigmAIQBackend( - backend="splunk", - processing_pipeline=my_custom_pipeline).create_backend() - -query = sigmaiq_backend.translate(copy(sigma_rule)) # Returns List of queries - -print("\nM365Defender Query with Custom Fieldmappings: ", end="\n\n") -pprint(query[0]) -print("\n-------------------") - +# %% This example shows how to use the SigmAIQ pySigma wrapper to provide custom field mappings for a backend +# %% This will allow you to translate specific field names to custom field names during rule translation + +# %% Import SigmAIQ +from sigmaiq import SigmAIQBackend, SigmAIQPipeline + +# %% Import pprint for pretty printing, and copy for copying rules +from pprint import pprint +from copy import copy +from typing import Dict, Union, List + +# %% A basic Sigma Rule in YAML str to convert to a query. +# %% SigmAIQ also accepts a rule in JSON/Dict format, SigmaRule objects, and SigmaCollection objects + +sigma_rule = """ +title: whoami Command +description: Detects a basic whoami commandline execution +logsource: + product: windows + category: process_creation +detection: + selection1: + - CommandLine|contains: 'whoami.exe' + condition: selection1 +""" + +# %% Create SigmAIQ backend translate the rule to a Microsoft 365 Defender query +sigmaiq_backend = SigmAIQBackend(backend="splunk").create_backend() +query = sigmaiq_backend.translate(copy(sigma_rule)) # Returns List of queries + +print("\nM365Defender Query: ", end="\n\n") +pprint(query[0]) +print("\n-------------------") + +# %% Create custom field mappings +# %% This will map the CommandLine field to a custom field named "CustomCommandLine" +custom_field_mappings: Dict[str, Union[str, List[str]]] = {"CommandLine": "CustomCommandLine"} +my_custom_pipeline = SigmAIQPipeline.from_fieldmap(custom_field_mappings, priority=0).create_pipeline() + +# %% Create SigmAIQ backend translate the rule to a Microsoft 365 Defender query with our custom field mappings +sigmaiq_backend = SigmAIQBackend(backend="splunk", processing_pipeline=my_custom_pipeline).create_backend() + +query = sigmaiq_backend.translate(copy(sigma_rule)) # Returns List of queries + +print("\nM365Defender Query with Custom Fieldmappings: ", end="\n\n") +pprint(query[0]) +print("\n-------------------") diff --git a/examples/llm_basic_usage.py b/examples/llm_basic_usage.py index 23202dd..758256b 100644 --- a/examples/llm_basic_usage.py +++ b/examples/llm_basic_usage.py @@ -1,36 +1,37 @@ -# %% This example will demonstrate how to use SigmAIQ to perform the following: -# %% 1. Download the latest Sigma Rule package release -# %% 2. Create embeddings of the Sigma Rules in the package -# %% 3. Create and save a VectorDB of the Sigma Rule embeddings -# %% 4. Use a similarity search on the VectorDB to find Sigma Rules similar to a provided query -from pprint import pprint - -# %% NOTE, this example uses OpenAI for embeddings. Ensure you have an OpenAI API key set in your environment variable -# %% OPENAI_API_KEY - -# %% Also ensure you have installed the correct requirements with: -# `pip install -r requirements/common.txt -r requirements/llm.txt` - - -# %% Import SigmAIQ LLM and OpenAIEmbeddings -from sigmaiq.llm.base import SigmaLLM - -# %% Create a SigmaLLM object with default settings. See the class docstring for more information -sigma_llm = SigmaLLM() - -# %% The `create_sigma_vectordb()` method will automatically do all the work for you :) (only run this once) -sigma_llm.create_sigma_vectordb(save=True) # Save locally to disk - -# %% Run a similarity search on the vectordb for encoded powershell commands and print top 3 results -query = "Encoded powershell commands" -matching_rules = sigma_llm.simple_search(query, k=3) -for matching_rule in matching_rules: - print(matching_rule.page_content, end="\n\n-------------------\n\n") - -# %% You can also load an existing vector store from disk (recommended) -sigma_llm.load_sigma_vectordb() - -query = "certutil" -matching_rules = sigma_llm.simple_search(query, k=3) -for matching_rule in matching_rules: - print(matching_rule.page_content, end="\n\n-------------------\n\n") +# %% This example will demonstrate how to use SigmAIQ to perform the following: +# %% 1. Download the latest Sigma Rule package release +# %% 2. Create embeddings of the Sigma Rules in the package +# %% 3. Create and save a VectorDB of the Sigma Rule embeddings +# %% 4. Use a similarity search on the VectorDB to find Sigma Rules similar to a provided query + +# %% NOTE, this example uses OpenAI for embeddings. Ensure you have an OpenAI API key set in your environment variable +# %% OPENAI_API_KEY + +# %% Also ensure you have installed the correct requirements with: +# `pip install -r requirements/common.txt -r requirements/llm.txt` + + +# %% Import SigmAIQ LLM and OpenAIEmbeddings +from sigmaiq.llm.base import SigmaLLM + +# %% Create a SigmaLLM object with default settings. See the class docstring for more information +from langchain_openai import OpenAIEmbeddings + +sigma_llm = SigmaLLM(embedding_model=OpenAIEmbeddings(model="text-embedding-3-large")) + +# %% The `create_sigma_vectordb()` method will automatically do all the work for you :) (only run this once) +sigma_llm.create_sigma_vectordb(save=True) # Save locally to disk + +# %% Run a similarity search on the vectordb for encoded powershell commands and print top 3 results +query = "Encoded powershell commands" +matching_rules = sigma_llm.simple_search(query, k=3) +for matching_rule in matching_rules: + print(matching_rule.page_content, end="\n\n-------------------\n\n") + +# %% You can also load an existing vector store from disk (recommended) +sigma_llm.load_sigma_vectordb() + +query = "certutil" +matching_rules = sigma_llm.simple_search(query, k=3) +for matching_rule in matching_rules: + print(matching_rule.page_content, end="\n\n-------------------\n\n") diff --git a/examples/llm_rule_translation_and_creation.py b/examples/llm_rule_translation_and_creation.py index 06cf9a0..6a1f6ff 100644 --- a/examples/llm_rule_translation_and_creation.py +++ b/examples/llm_rule_translation_and_creation.py @@ -1,93 +1,110 @@ -# %% This example will demonstrate how to create a Sigma langchain agent chatbot, which can perform various tasks like -# %% automatically translate a rule for you, and create new rules from a users input. - -# %% Import required SigmAIQ classes and functions -from sigmaiq.llm.toolkits.base import create_sigma_agent -from sigmaiq.llm.base import SigmaLLM - -# %% Ensure we have our Sigma vector store setup with our base LLM class -sigma_llm = SigmaLLM() - -try: - sigma_llm.load_sigma_vectordb() -except Exception as e: - print(e) - print("Creating new Sigma VectorDB") - sigma_llm.create_sigma_vectordb(save=True) - -# %% Create a Sigma Agent Executor, and pass it our Sigma VectorDB -sigma_agent_executor = create_sigma_agent(sigma_vectorstore=sigma_llm.sigmadb) - -print("\n--------\nRULE TRANSLATION\n--------\n") -# %% RULE TRANSLATION -# %% Have the agent automatically translate a Sigma rule to a Splunk query with the splunk_cim_dm pipeline -user_input = "Convert this Sigma rule to a Splunk query using the 'splunk_cim_dm' pipeline: \n\n" + \ - "title: whoami Command\n" + \ - "description: Detects a basic whoami commandline execution\n" + \ - "logsource:\n" + \ - " product: windows\n" + \ - " category: process_creation\n" + \ - "detection:\n" + \ - " selection1:\n" + \ - " - CommandLine|contains: 'whoami.exe'\n" + \ - " condition: selection1" - -answer = sigma_agent_executor.invoke({"input": user_input}) -print(f"\n\nQUESTION:\n {user_input}", end="\n\n") -print("ANSWER: \n") -print(answer.get('output'), end="\n\n") - -# %% RULE SEARCHING -# %% The agent will find official Sigma rules in the Sigma vector store based on the context of the users input. -print("\n--------\nRULE SEARCHING\n--------\n") - -user_input = ("What Sigma Rule can detect a process creation event where the parent process is word.exe and the child " - "process is cmd.exe?") - -answer = sigma_agent_executor.invoke({"input": user_input}) -print(f"QUESTION:\n {user_input}", end="\n\n") -print("ANSWER: \n") -print(answer.get('output'), end="\n\n") - -# %% QUERY TO SIGMA RULE CONVERSION -# %% If you already have a query and want to convert it back to a Sigma Rule -print("\n--------\nQUERY TO SIGMA RULE \n--------\n") - -user_input = """ -Convert this Microsoft365Defender query into a Sigma Rule: -DeviceProcessEvents -| where ((ProcessCommandLine contains "powershell.exe -enc" or ProcessCommandLine contains "cmd.exe /c" or ProcessCommandLine contains "rundll32.exe") or (InitiatingProcessFolderPath endswith "\\explorer.exe" or InitiatingProcessFolderPath endswith "\\svchost.exe")) and (not((ProcessCommandLine contains "schtasks" or ProcessCommandLine contains "tasklist"))) - -""" - -answer = sigma_agent_executor.invoke({"input": user_input}) -print(f"\n\nQUESTION:\n {user_input}", end="\n\n") -print("ANSWER: \n") -print(answer.get('output'), end="\n\n") - -# %% RULE CREATION -# %% The agent will take the user input, look up similar Sigma Rules in the Sigma vector store, then create a brand -# %% new rule based on the context of the users input and the similar Sigma Rules. -print("\n--------\nRULE CREATION\n--------\n") - -user_input = ("Create a Windows process creation Sigma Rule for certutil downloading a file " - "from definitely-not-malware.com, then translate it to a Microsoft 365 Defender query.") - -answer = sigma_agent_executor.invoke({"input": user_input}) -print(f"QUESTION:\n {user_input}", end="\n\n") -print("ANSWER: \n") -print(answer.get('output'), end="\n\n") - - -# %% RULE CREATION + TRANSLATION -# %% The agent will take the user input, look up similar Sigma Rules in the Sigma vector store, then create a brand -# %% new rule based on the context of the users input and the similar Sigma Rules. Then convert it to a M365 query -print("\n--------\nRULE CREATION + TRANSLATION\n--------\n") - -user_input = ("Create a Windows process creation Sigma Rule for certutil downloading a file " - "from definitely-not-malware.com, then translate it to a Microsoft 365 Defender query.") - -answer = sigma_agent_executor.invoke({"input": user_input}) -print(f"QUESTION:\n {user_input}", end="\n\n") -print("ANSWER: \n") -print(answer.get('output'), end="\n\n") +# %% This example will demonstrate how to create a Sigma langchain agent chatbot, which can perform various tasks like +# %% automatically translate a rule for you, and create new rules from a users input. +import asyncio + +from langchain_openai import OpenAIEmbeddings + +from sigmaiq.llm.base import SigmaLLM + +# %% Import required SigmAIQ classes and functions +from sigmaiq.llm.toolkits.base import create_sigma_agent + +# %% Ensure we have our Sigma vector store setup with our base LLM class +sigma_llm = SigmaLLM(embedding_model=OpenAIEmbeddings(model="text-embedding-3-large")) + +try: + sigma_llm.load_sigma_vectordb() +except Exception as e: + print(e) + print("Creating new Sigma VectorDB") + sigma_llm.create_sigma_vectordb(save=True) + +# %% Create a Sigma Agent Executor, and pass it our Sigma VectorDB +sigma_agent_executor = create_sigma_agent(sigma_vectorstore=sigma_llm.sigmadb) + + +async def main(): + print("\n--------\nRULE TRANSLATION\n--------\n") + # %% RULE TRANSLATION + # %% Have the agent automatically translate a Sigma rule to a Splunk query with the splunk_cim_dm pipeline + user_input = ( + "Convert this Sigma rule to a Splunk query using the 'splunk_cim_dm' pipeline: \n\n" + + "title: whoami Command\n" + + "description: Detects a basic whoami commandline execution\n" + + "logsource:\n" + + " product: windows\n" + + " category: process_creation\n" + + "detection:\n" + + " selection1:\n" + + " - CommandLine|contains: 'whoami.exe'\n" + + " condition: selection1" + ) + + answer = await sigma_agent_executor.ainvoke({"input": user_input}) + print(f"\n\nQUESTION:\n {user_input}", end="\n\n") + print("ANSWER: \n") + print(answer.get("output"), end="\n\n") + + # %% RULE SEARCHING + # %% The agent will find official Sigma rules in the Sigma vector store based on the context of the users input. + print("\n--------\nRULE SEARCHING\n--------\n") + + user_input = ( + "What Sigma Rule can detect a process creation event where the parent process is word.exe and the child " + "process is cmd.exe?" + ) + + answer = await sigma_agent_executor.ainvoke({"input": user_input}) + print(f"QUESTION:\n {user_input}", end="\n\n") + print("ANSWER: \n") + print(answer.get("output"), end="\n\n") + + # %% QUERY TO SIGMA RULE CONVERSION + # %% If you already have a query and want to convert it back to a Sigma Rule + print("\n--------\nQUERY TO SIGMA RULE \n--------\n") + + user_input = """ +Convert this Microsoft XDR KQL query into a Sigma Rule: +DeviceProcessEvents +| where ((ProcessCommandLine contains "powershell.exe -enc" or ProcessCommandLine contains "cmd.exe /c" or ProcessCommandLine contains "rundll32.exe") or (InitiatingProcessFolderPath endswith "\\explorer.exe" or InitiatingProcessFolderPath endswith "\\svchost.exe")) and (not((ProcessCommandLine contains "schtasks" or ProcessCommandLine contains "tasklist"))) + +""" + + answer = await sigma_agent_executor.ainvoke({"input": user_input}) + print(f"\n\nQUESTION:\n {user_input}", end="\n\n") + print("ANSWER: \n") + print(answer.get("output"), end="\n\n") + + # %% RULE CREATION + # %% The agent will take the user input, look up similar Sigma Rules in the Sigma vector store, then create a brand + # %% new rule based on the context of the users input and the similar Sigma Rules. + print("\n--------\nRULE CREATION\n--------\n") + + user_input = ( + "Create a Windows process creation Sigma Rule for certutil downloading a file " + "from definitely-not-malware.com" + ) + + answer = await sigma_agent_executor.ainvoke({"input": user_input}) + print(f"QUESTION:\n {user_input}", end="\n\n") + print("ANSWER: \n") + print(answer.get("output"), end="\n\n") + + # %% RULE CREATION + TRANSLATION + # %% The agent will take the user input, look up similar Sigma Rules in the Sigma vector store, then create a brand + # %% new rule based on the context of the users input and the similar Sigma Rules. Then convert it to a M365 query + print("\n--------\nRULE CREATION + TRANSLATION\n--------\n") + + user_input = ( + "Create a Windows process creation Sigma Rule for certutil downloading a file " + "from definitely-not-malware.com, then translate it to a Microsoft XDR KQL query." + ) + + answer = await sigma_agent_executor.ainvoke({"input": user_input}) + print(f"QUESTION:\n {user_input}", end="\n\n") + print("ANSWER: \n") + print(answer.get("output"), end="\n\n") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/translate_sigma_rules.py b/examples/translate_sigma_rules.py index 3cb1ef8..cf18183 100644 --- a/examples/translate_sigma_rules.py +++ b/examples/translate_sigma_rules.py @@ -1,77 +1,77 @@ -# %% This example shows how to use the SigmAIQ pySigma wrapper to easily translate Sigma rules to queries -# %% easily, without having to worry about installing and configuring the correct backends, pipelines and other details. - - -# %% Import SigmAIQ -from sigmaiq import SigmAIQBackend - -# %% Import pprint for pretty printing, and copy for copying rules -from pprint import pprint -from copy import copy - -# %% A basic Sigma Rule in YAML str to convert to a query. -# %% SigmAIQ also accepts a rule in JSON/Dict format, SigmaRule objects, and SigmaCollection objects - -sigma_rule = """ -title: whoami Command -description: Detects a basic whoami commandline execution -logsource: - product: windows - category: process_creation -detection: - selection1: - - CommandLine|contains: 'whoami.exe' - condition: selection1 -""" - -# %% BACKENDS -# %% Show the available supported backends -print("Supported Backends:", end="\n\n") -pprint(SigmAIQBackend.display_available_backends()) -print("\n-------------------") - -# %% Create SigmAIQ backend translate the rule to a Microsoft 365 Defender query -# %% SigmAIQ will automatically select the best pipeline for the backend -sigmaiq_backend = SigmAIQBackend(backend="microsoft365defender").create_backend() -query = sigmaiq_backend.translate(copy(sigma_rule)) # Returns List of queries - -print("\nM365Defender Query: ", end="\n\n") -pprint(query[0]) -print("\n-------------------") - -# %% PIPELINES -# %% Show the available pipelines with each backend -print("Available Pipelines:", end="\n\n") -pprint(SigmAIQBackend.display_all_associated_pipelines()) -print("\n-------------------") - -# %% Create SigmAIQ backend translate the rule to a Splunk search with the CIM pipeline -sigmaiq_backend = SigmAIQBackend(backend="splunk", processing_pipeline="splunk_cim_dm").create_backend() -query = sigmaiq_backend.translate(copy(sigma_rule)) - -print("\nSplunk CIM Query: ", end="\n\n") -pprint(query[0]) -print("\n-------------------") - -# %% OUTPUT FORMATS -# %% Show the available output formats with each backend -print("\nAvailable Output Formats:", end="\n\n") -pprint(SigmAIQBackend.display_backends_and_outputs()) -print("\n-------------------") - -# %% Change the output_format to an Enterprise Security Correlation Search stanza -sigmaiq_backend.set_output_format("stanza") -query = sigmaiq_backend.translate(copy(sigma_rule)) - -print("\nSplunk CIM Query, Stanza Output: ", end="\n\n") -pprint(query[0]) -print("\n-------------------") - - -# %% You can also translate a Sigma rule to all supported backend, pipeline, and output format combinations at once. -# %% Any combination that is not supported will not be included in the results -# %% This is useful for testing and comparing the output of different backends and pipelines -queries = SigmAIQBackend.create_all_and_translate(copy(sigma_rule)) - -print("\n All Translations: ", end="\n\n") -pprint(queries) \ No newline at end of file +# %% This example shows how to use the SigmAIQ pySigma wrapper to easily translate Sigma rules to queries +# %% easily, without having to worry about installing and configuring the correct backends, pipelines and other details. + + +# %% Import SigmAIQ +from sigmaiq import SigmAIQBackend + +# %% Import pprint for pretty printing, and copy for copying rules +from pprint import pprint +from copy import copy + +# %% A basic Sigma Rule in YAML str to convert to a query. +# %% SigmAIQ also accepts a rule in JSON/Dict format, SigmaRule objects, and SigmaCollection objects + +sigma_rule = """ +title: whoami Command +description: Detects a basic whoami commandline execution +logsource: + product: windows + category: process_creation +detection: + selection1: + - CommandLine|contains: 'whoami.exe' + condition: selection1 +""" + +# %% BACKENDS +# %% Show the available supported backends +print("Supported Backends:", end="\n\n") +pprint(SigmAIQBackend.display_available_backends()) +print("\n-------------------") + +# %% Create SigmAIQ backend translate the rule to a Microsoft XDR query +# %% SigmAIQ will automatically select the best pipeline for the backend +sigmaiq_backend = SigmAIQBackend(backend="microsoft_xdr").create_backend() +query = sigmaiq_backend.translate(copy(sigma_rule)) # Returns List of queries + +print("\nMicrosoft XDR KQL Query: ", end="\n\n") +pprint(query[0]) +print("\n-------------------") + +# %% PIPELINES +# %% Show the available pipelines with each backend +print("Available Pipelines:", end="\n\n") +pprint(SigmAIQBackend.display_all_associated_pipelines()) +print("\n-------------------") + +# %% Create SigmAIQ backend translate the rule to a Splunk search with the CIM pipeline +sigmaiq_backend = SigmAIQBackend(backend="splunk", processing_pipeline="splunk_cim_dm").create_backend() +query = sigmaiq_backend.translate(copy(sigma_rule)) + +print("\nSplunk CIM Query: ", end="\n\n") +pprint(query[0]) +print("\n-------------------") + +# %% OUTPUT FORMATS +# %% Show the available output formats with each backend +print("\nAvailable Output Formats:", end="\n\n") +pprint(SigmAIQBackend.display_backends_and_outputs()) +print("\n-------------------") + +# %% Change the output_format to an Enterprise Security Correlation Search stanza +sigmaiq_backend.set_output_format("stanza") +query = sigmaiq_backend.translate(copy(sigma_rule)) + +print("\nSplunk CIM Query, Stanza Output: ", end="\n\n") +pprint(query[0]) +print("\n-------------------") + + +# %% You can also translate a Sigma rule to all supported backend, pipeline, and output format combinations at once. +# %% Any combination that is not supported will not be included in the results +# %% This is useful for testing and comparing the output of different backends and pipelines +queries = SigmAIQBackend.create_all_and_translate(copy(sigma_rule)) +# We won't print it, as its a lot of output +# print("\n All Translations: ", end="\n\n") +# pprint(queries) diff --git a/generate_readme_tables.py b/generate_readme_tables.py new file mode 100644 index 0000000..a7f88f9 --- /dev/null +++ b/generate_readme_tables.py @@ -0,0 +1,52 @@ +import sigmaiq.sigmaiq_backend_factory as backend_factory +import sigmaiq.sigmaiq_pipeline_factory as pipeline_factory + +def generate_backends_table(): + backends = backend_factory.AVAILABLE_BACKENDS + associated_pipelines = backend_factory.SigmAIQBackend.display_all_associated_pipelines() + table = "| Backend Option | Description | Associated Pipelines | Default Pipeline |\n|-----------------|-------------|----------------------|-------------------|\n" + for backend, description in backends.items(): + pipelines = associated_pipelines.get(backend, {}) + pipeline_list = "
".join(pipelines.keys()) + default_pipeline = next(iter(pipelines.keys())) if pipelines else "N/A" + table += f"| {backend} | {description} | {pipeline_list} | {default_pipeline} |\n" + return table + +def generate_output_formats_table(): + backend_formats = backend_factory.SigmAIQBackend.display_backends_and_outputs() + table = "| Backend Option | Output Format Option | Description |\n|-----------------|------------------------|-------------|\n" + for backend, data in backend_formats.items(): + output_formats = [] + descriptions = [] + for format, description in data['output_formats'].items(): + output_formats.append(format) + descriptions.append(description) + + output_formats_str = "
".join(output_formats) + descriptions_str = "
".join(descriptions) + table += f"| {backend} | {output_formats_str} | {descriptions_str} |\n" + return table + +def generate_pipelines_table(): + pipelines = pipeline_factory.AVAILABLE_PIPELINES + table = "| Pipeline Option | Description | Display Name |\n|------------------|-------------|---------------|\n" + for pipeline, data in pipelines.items(): + description = data['description'] + display_name = data['display_name'] + table += f"| {pipeline} | {description} | {display_name} |\n" + return table + +def main(): + backends_table = generate_backends_table() + output_formats_table = generate_output_formats_table() + pipelines_table = generate_pipelines_table() + + print("## Available Backends\n") + print(backends_table) + print("\n## Backend Output Formats\n") + print(output_formats_table) + print("\n## Available Named Pipelines\n") + print(pipelines_table) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/poetry.lock b/poetry.lock index 3cc62f5..cd14a17 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"importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] [metadata] lock-version = "2.0" -python-versions = ">=3.8.1, <=3.11.9" -content-hash = "cb4d6f6e4a1f657bd6459dde8d9c2e5479c66e7affde0d8c854428523fe68c91" +python-versions = ">=3.9, <3.13" +content-hash = "ecb10204f3958e34dd6d23e279a7d3f94f6ecc6aa3c2d31d50a3437ffb6de067" diff --git a/pyproject.toml b/pyproject.toml index f8ef0e8..155f214 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "SigmAIQ" -version = "0.4.1" +version = "0.4.2" description = "Wrapper and tools for pySigma and Sigma rules" authors = ["Stephen Lincoln ", "AttackIQ "] readme = "README.md" @@ -20,41 +20,46 @@ packages = [ ] [tool.poetry.dependencies] -python = ">=3.8.1, <=3.11.9" -pysigma = "0.10.10" -certifi = ">=2023.7.22" -pysigma-backend-carbonblack = "^0.1.6" -pysigma-backend-cortexxdr = "0.1.2" -pysigma-backend-elasticsearch = "1.0.10" -pysigma-backend-insightidr = "0.2.2" -pysigma-backend-loki = "0.10.3" -pysigma-backend-microsoft365defender = "0.2.6" -pysigma-backend-opensearch = "1.0.1" -pysigma-backend-qradar-aql = "^0.3.2" -pysigma-backend-sentinelone = "^0.1.3" -pysigma-backend-splunk = "1.0.3" -pysigma-backend-stix2 = "^0.2.0" -pysigma-pipeline-crowdstrike = "1.0.1" -pysigma-pipeline-sysmon = "1.0.3" -pysigma-pipeline-windows = "1.1.1" +python = ">=3.9, <3.13" +pysigma = "0.11.14" +certifi = "^2024.8.30" +pysigma-backend-carbonblack = "0.1.8" +pysigma-backend-cortexxdr = "0.1.4" +pysigma-backend-crowdstrike = "2.0.0" +pysigma-backend-elasticsearch = "1.1.2" +pysigma-backend-insightidr = "0.2.4" +pysigma-backend-kusto = "0.4.2" +pysigma-backend-loki = "0.12.2" +pysigma-backend-netwitness = "0.1.3" +pysigma-backend-opensearch = "1.0.2" +pysigma-backend-qradar-aql = "0.3.2" +pysigma-backend-sentinelone = "0.1.3" +pysigma-backend-splunk = "1.1.0" +pysigma-backend-stix2 = "0.2.0" +pysigma-pipeline-sysmon = "1.0.4" +pysigma-pipeline-windows = "1.2.0" importlib-resources = "^5.13.0" [tool.poetry.group.llm] optional = true [tool.poetry.group.llm.dependencies] -aiohttp = "^3.9.4" -langchain = "^0.0.335" -openai = "^1.2.4" -tiktoken = "^0.5.1" -faiss-cpu = "^1.7.4" +langchain = "^0.2.9" +openai = "*" +tiktoken = "*" +faiss-cpu = "*" +langchain-community = "^0.2.7" +langchain-openai = "^0.1.17" +langchain-core = "^0.2.20" +[tool.poetry.group.dev] +optional = true [tool.poetry.group.dev.dependencies] -pytest = "^7.4.0" -pytest-cov = "^4.1.0" -black = "^24.4.2" -ruff = "^0.0.286" +pytest = "^8.3.3" +pytest-cov = "^5.0.0" +black = "^24.8.0" +ruff = "^0.6.8" [build-system] requires = ["poetry-core"] @@ -62,13 +67,50 @@ build-backend = "poetry.core.masonry.api" [tool.black] line-length = 120 +target-version = ['py311'] + +[tool.ruff] +# Exclude a variety of commonly ignored directories. +exclude = [ + ".bzr", + ".direnv", + ".eggs", + ".git", + ".git-rewrite", + ".hg", + ".ipynb_checkpoints", + ".mypy_cache", + ".nox", + ".pants.d", + ".pyenv", + ".pytest_cache", + ".pytype", + ".ruff_cache", + ".svn", + ".tox", + ".venv", + ".vscode", + "__pypackages__", + "_build", + "buck-out", + "build", + "dist", + "node_modules", + "site-packages", + "venv", + ".venv" +] + +# Same as Black. +line-length = 120 + +# Assume Python 3.11 +target-version = "py311" [tool.coverage.run] omit = [ # omit anything in a tests directory "tests/*", - # omit everything in LLM - "sigmaiq/llm/*", # omit globals "sigmaiq/globals.py", ] \ No newline at end of file diff --git a/requirements/common.txt b/requirements/common.txt index 60512e3..f1e43f2 100644 --- a/requirements/common.txt +++ b/requirements/common.txt @@ -1,28 +1,29 @@ -certifi==2024.2.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -charset-normalizer==3.3.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -idna==3.7 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -importlib-resources==5.13.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -jinja2==3.1.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -markupsafe==2.1.5 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -packaging==22.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pyparsing==3.1.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma-backend-carbonblack==0.1.6 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma-backend-cortexxdr==0.1.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma-backend-elasticsearch==1.0.10 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma-backend-insightidr==0.2.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma-backend-loki==0.10.3 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma-backend-microsoft365defender==0.2.6 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma-backend-opensearch==1.0.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma-backend-qradar-aql==0.3.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma-backend-sentinelone==0.1.3 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma-backend-splunk==1.0.3 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma-backend-stix2==0.2.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma-pipeline-crowdstrike==1.0.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma-pipeline-sysmon==1.0.3 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma-pipeline-windows==1.1.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pysigma==0.10.10 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pyyaml==6.0.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -regex==2023.12.25 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -requests==2.31.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -urllib3==2.2.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -zipp==3.18.2 ; python_full_version >= "3.8.1" and python_version < "3.10" +certifi==2024.8.30 ; python_version >= "3.9" and python_version < "3.13" +charset-normalizer==3.4.0 ; python_version >= "3.9" and python_version < "3.13" +idna==3.10 ; python_version >= "3.9" and python_version < "3.13" +importlib-resources==5.13.0 ; python_version >= "3.9" and python_version < "3.13" +jinja2==3.1.4 ; python_version >= "3.9" and python_version < "3.13" +markupsafe==3.0.1 ; python_version >= "3.9" and python_version < "3.13" +packaging==24.1 ; python_version >= "3.9" and python_version < "3.13" +pyparsing==3.1.4 ; python_version >= "3.9" and python_version < "3.13" +pysigma-backend-carbonblack==0.1.8 ; python_version >= "3.9" and python_version < "3.13" +pysigma-backend-cortexxdr==0.1.4 ; python_version >= "3.9" and python_version < "3.13" +pysigma-backend-crowdstrike==2.0.0 ; python_version >= "3.9" and python_version < "3.13" +pysigma-backend-elasticsearch==1.1.2 ; python_version >= "3.9" and python_version < "3.13" +pysigma-backend-insightidr==0.2.4 ; python_version >= "3.9" and python_version < "3.13" +pysigma-backend-kusto==0.4.2 ; python_version >= "3.9" and python_version < "3.13" +pysigma-backend-loki==0.12.2 ; python_version >= "3.9" and python_version < "3.13" +pysigma-backend-netwitness==0.1.3 ; python_version >= "3.9" and python_version < "3.13" +pysigma-backend-opensearch==1.0.2 ; python_version >= "3.9" and python_version < "3.13" +pysigma-backend-qradar-aql==0.3.2 ; python_version >= "3.9" and python_version < "3.13" +pysigma-backend-sentinelone==0.1.3 ; python_version >= "3.9" and python_version < "3.13" +pysigma-backend-splunk==1.1.0 ; python_version >= "3.9" and python_version < "3.13" +pysigma-backend-stix2==0.2.0 ; python_version >= "3.9" and python_version < "3.13" +pysigma-pipeline-sysmon==1.0.4 ; python_version >= "3.9" and python_version < "3.13" +pysigma-pipeline-windows==1.2.0 ; python_version >= "3.9" and python_version < "3.13" +pysigma==0.11.14 ; python_version >= "3.9" and python_version < "3.13" +pyyaml==6.0.2 ; python_version >= "3.9" and python_version < "3.13" +regex==2023.12.25 ; python_version >= "3.9" and python_version < "3.13" +requests==2.32.3 ; python_version >= "3.9" and python_version < "3.13" +urllib3==2.2.3 ; python_version >= "3.9" and python_version < "3.13" +zipp==3.20.2 ; python_version >= "3.9" and python_version < "3.10" diff --git a/requirements/dev.txt b/requirements/dev.txt index 2ef8d66..008c7eb 100644 --- a/requirements/dev.txt +++ b/requirements/dev.txt @@ -1,16 +1,16 @@ -black==24.4.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -click==8.1.7 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -colorama==0.4.6 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" and sys_platform == "win32" or python_full_version >= "3.8.1" and python_full_version <= "3.11.9" and platform_system == "Windows" -coverage[toml]==7.5.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -exceptiongroup==1.2.1 ; python_full_version >= "3.8.1" and python_version < "3.11" -iniconfig==2.0.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -mypy-extensions==1.0.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -packaging==22.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pathspec==0.12.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -platformdirs==4.2.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pluggy==1.5.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pytest-cov==4.1.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pytest==7.4.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -ruff==0.0.286 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -tomli==2.0.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.0a6" -typing-extensions==4.11.0 ; python_full_version >= "3.8.1" and python_version < "3.11" +black==24.10.0 ; python_version >= "3.9" and python_version < "3.13" +click==8.1.7 ; python_version >= "3.9" and python_version < "3.13" +colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows") +coverage[toml]==7.6.2 ; python_version >= "3.9" and python_version < "3.13" +exceptiongroup==1.2.2 ; python_version >= "3.9" and python_version < "3.11" +iniconfig==2.0.0 ; python_version >= "3.9" and python_version < "3.13" +mypy-extensions==1.0.0 ; python_version >= "3.9" and python_version < "3.13" +packaging==24.1 ; python_version >= "3.9" and python_version < "3.13" +pathspec==0.12.1 ; python_version >= "3.9" and python_version < "3.13" +platformdirs==4.3.6 ; python_version >= "3.9" and python_version < "3.13" +pluggy==1.5.0 ; python_version >= "3.9" and python_version < "3.13" +pytest-cov==5.0.0 ; python_version >= "3.9" and python_version < "3.13" +pytest==8.3.3 ; python_version >= "3.9" and python_version < "3.13" +ruff==0.6.9 ; python_version >= "3.9" and python_version < "3.13" +tomli==2.0.2 ; python_version >= "3.9" and python_full_version <= "3.11.0a6" +typing-extensions==4.12.2 ; python_version >= "3.9" and python_version < "3.11" diff --git a/requirements/llm.txt b/requirements/llm.txt index 33a6b33..2c053f5 100644 --- a/requirements/llm.txt +++ b/requirements/llm.txt @@ -1,43 +1,52 @@ -aiohttp==3.9.5 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -aiosignal==1.3.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -annotated-types==0.6.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -anyio==3.7.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -async-timeout==4.0.3 ; python_full_version >= "3.8.1" and python_version < "3.11" -attrs==23.2.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -certifi==2024.2.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -charset-normalizer==3.3.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -colorama==0.4.6 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" and platform_system == "Windows" -dataclasses-json==0.6.6 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -distro==1.9.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -exceptiongroup==1.2.1 ; python_full_version >= "3.8.1" and python_version < "3.11" -faiss-cpu==1.8.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -frozenlist==1.4.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -greenlet==3.0.3 ; platform_machine == "aarch64" and python_full_version >= "3.8.1" and python_full_version <= "3.11.9" or platform_machine == "ppc64le" and python_full_version >= "3.8.1" and python_full_version <= "3.11.9" or platform_machine == "x86_64" and python_full_version >= "3.8.1" and python_full_version <= "3.11.9" or platform_machine == "amd64" and python_full_version >= "3.8.1" and python_full_version <= "3.11.9" or platform_machine == "AMD64" and python_full_version >= "3.8.1" and python_full_version <= "3.11.9" or platform_machine == "win32" and python_full_version >= "3.8.1" and python_full_version <= "3.11.9" or platform_machine == "WIN32" and python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -h11==0.14.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -httpcore==1.0.5 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -httpx==0.27.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -idna==3.7 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -jsonpatch==1.33 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -jsonpointer==2.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -langchain==0.0.335 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -langsmith==0.0.92 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -marshmallow==3.21.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -multidict==6.0.5 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -mypy-extensions==1.0.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -numpy==1.24.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -openai==1.30.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -packaging==22.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pydantic-core==2.18.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pydantic==2.7.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -pyyaml==6.0.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -regex==2023.12.25 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -requests==2.31.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -sniffio==1.3.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -sqlalchemy==2.0.30 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -tenacity==8.3.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -tiktoken==0.5.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -tqdm==4.66.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -typing-extensions==4.11.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -typing-inspect==0.9.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -urllib3==2.2.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" -yarl==1.9.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +aiohappyeyeballs==2.4.3 ; python_version >= "3.9" and python_version < "3.13" +aiohttp==3.10.9 ; python_version >= "3.9" and python_version < "3.13" +aiosignal==1.3.1 ; python_version >= "3.9" and python_version < "3.13" +annotated-types==0.7.0 ; python_version >= "3.9" and python_version < "3.13" +anyio==4.6.0 ; python_version >= "3.9" and python_version < "3.13" +async-timeout==4.0.3 ; python_version >= "3.9" and python_version < "3.11" +attrs==24.2.0 ; python_version >= "3.9" and python_version < "3.13" +certifi==2024.8.30 ; python_version >= "3.9" and python_version < "3.13" +charset-normalizer==3.4.0 ; python_version >= "3.9" and python_version < "3.13" +colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and platform_system == "Windows" +dataclasses-json==0.6.7 ; python_version >= "3.9" and python_version < "3.13" +distro==1.9.0 ; python_version >= "3.9" and python_version < "3.13" +exceptiongroup==1.2.2 ; python_version >= "3.9" and python_version < "3.11" +faiss-cpu==1.9.0 ; python_version >= "3.9" and python_version < "3.13" +frozenlist==1.4.1 ; python_version >= "3.9" and python_version < "3.13" +greenlet==3.1.1 ; python_version < "3.13" and (platform_machine == "aarch64" or platform_machine == "ppc64le" or platform_machine == "x86_64" or platform_machine == "amd64" or platform_machine == "AMD64" or platform_machine == "win32" or platform_machine == "WIN32") and python_version >= "3.9" +h11==0.14.0 ; python_version >= "3.9" and python_version < "3.13" +httpcore==1.0.6 ; python_version >= "3.9" and python_version < "3.13" +httpx==0.27.2 ; python_version >= "3.9" and python_version < "3.13" +idna==3.10 ; python_version >= "3.9" and python_version < "3.13" +jiter==0.6.1 ; python_version >= "3.9" and python_version < "3.13" +jsonpatch==1.33 ; python_version >= "3.9" and python_version < "3.13" +jsonpointer==3.0.0 ; python_version >= "3.9" and python_version < "3.13" +langchain-community==0.2.17 ; python_version >= "3.9" and python_version < "3.13" +langchain-core==0.2.41 ; python_version >= "3.9" and python_version < "3.13" +langchain-openai==0.1.25 ; python_version >= "3.9" and python_version < "3.13" +langchain-text-splitters==0.2.4 ; python_version >= "3.9" and python_version < "3.13" +langchain==0.2.16 ; python_version >= "3.9" and python_version < "3.13" +langsmith==0.1.133 ; python_version >= "3.9" and python_version < "3.13" +marshmallow==3.22.0 ; python_version >= "3.9" and python_version < "3.13" +multidict==6.1.0 ; python_version >= "3.9" and python_version < "3.13" +mypy-extensions==1.0.0 ; python_version >= "3.9" and python_version < "3.13" +numpy==1.26.4 ; python_version >= "3.9" and python_version < "3.13" +openai==1.51.2 ; python_version >= "3.9" and python_version < "3.13" +orjson==3.10.7 ; python_version >= "3.9" and python_version < "3.13" +packaging==24.1 ; python_version >= "3.9" and python_version < "3.13" +propcache==0.2.0 ; python_version >= "3.9" and python_version < "3.13" +pydantic-core==2.23.4 ; python_version >= "3.9" and python_version < "3.13" +pydantic==2.9.2 ; python_version >= "3.9" and python_version < "3.13" +pyyaml==6.0.2 ; python_version >= "3.9" and python_version < "3.13" +regex==2023.12.25 ; python_version >= "3.9" and python_version < "3.13" +requests-toolbelt==1.0.0 ; python_version >= "3.9" and python_version < "3.13" +requests==2.32.3 ; python_version >= "3.9" and python_version < "3.13" +sniffio==1.3.1 ; python_version >= "3.9" and python_version < "3.13" +sqlalchemy==2.0.35 ; python_version >= "3.9" and python_version < "3.13" +tenacity==8.5.0 ; python_version >= "3.9" and python_version < "3.13" +tiktoken==0.8.0 ; python_version >= "3.9" and python_version < "3.13" +tqdm==4.66.5 ; python_version >= "3.9" and python_version < "3.13" +typing-extensions==4.12.2 ; python_version >= "3.9" and python_version < "3.13" +typing-inspect==0.9.0 ; python_version >= "3.9" and python_version < "3.13" +urllib3==2.2.3 ; python_version >= "3.9" and python_version < "3.13" +yarl==1.14.0 ; python_version >= "3.9" and python_version < "3.13" diff --git a/sigmaiq/backends/crowdstrike/__init__.py b/sigmaiq/backends/crowdstrike/__init__.py index 70777d6..27e03aa 100644 --- a/sigmaiq/backends/crowdstrike/__init__.py +++ b/sigmaiq/backends/crowdstrike/__init__.py @@ -1 +1 @@ -from .crowdstrike import SigmAIQCrowdstrikeSplunkBackend +from .crowdstrike import SigmAIQCrowdstrikeSplunkBackend, SigmAIQCrowdstrikeLogscaleBackend diff --git a/sigmaiq/backends/crowdstrike/crowdstrike.py b/sigmaiq/backends/crowdstrike/crowdstrike.py index c5db0c1..6aa1021 100644 --- a/sigmaiq/backends/crowdstrike/crowdstrike.py +++ b/sigmaiq/backends/crowdstrike/crowdstrike.py @@ -1,9 +1,21 @@ from sigma.backends.splunk import SplunkBackend +from sigma.backends.crowdstrike import LogScaleBackend from sigmaiq.backends.sigmaiq_abstract_backend import AbstractGenericSigmAIQBackendClass class SigmAIQCrowdstrikeSplunkBackend(AbstractGenericSigmAIQBackendClass, SplunkBackend): + """SigmAIQ backend interface for the pySigma Splunk Backend library to translate a SigmaRule object + to a Splunk search query with the Crowdstrike FDR format""" custom_formats = {} - associated_pipelines = ["crowdstrike"] - default_pipeline = "crowdstrike" + associated_pipelines = ["crowdstrike_fdr"] + default_pipeline = "crowdstrike_fdr" formats = {"default": SplunkBackend.formats["default"]} + + +class SigmAIQCrowdstrikeLogscaleBackend(AbstractGenericSigmAIQBackendClass, LogScaleBackend): + """SigmAIQ backend interface for the pySigma Logscale Backend library to translate a SigmaRule object + to a Logscale search query with the Crowdstrike Falcon format""" + custom_formats = {} + associated_pipelines = ["crowdstrike_falcon"] + default_pipeline = "crowdstrike_falcon" + formats = {"default": LogScaleBackend.formats["default"]} diff --git a/sigmaiq/backends/elasticsearch/elasticsearch.py b/sigmaiq/backends/elasticsearch/elasticsearch.py index 109ac89..879846e 100644 --- a/sigmaiq/backends/elasticsearch/elasticsearch.py +++ b/sigmaiq/backends/elasticsearch/elasticsearch.py @@ -3,6 +3,15 @@ class SigmAIQElasticsearchBackend(AbstractGenericSigmAIQBackendClass, LuceneBackend): + """SigmAIQ backend interface for the pySigma Elasticsearch Backend library to translate a SigmaRule object + to an Elasticsearch search query""" custom_formats = {} - associated_pipelines = ["ecs_windows", "ecs_windows_old", "ecs_zeek_beats", "ecs_zeek_corelight", "zeek_raw"] + associated_pipelines = [ + "ecs_windows", + "ecs_kubernetes", + "ecs_windows_old", + "ecs_zeek_beats", + "ecs_zeek_corelight", + "zeek_raw", + ] default_pipeline = "ecs_windows" diff --git a/sigmaiq/backends/kusto/__init__.py b/sigmaiq/backends/kusto/__init__.py new file mode 100644 index 0000000..39bdf2d --- /dev/null +++ b/sigmaiq/backends/kusto/__init__.py @@ -0,0 +1 @@ +from .kusto import SigmAIQDefenderXDRBackend, SigmAIQSentinelASIMBackend, SigmAIQAzureMonitorBackend diff --git a/sigmaiq/backends/kusto/kusto.py b/sigmaiq/backends/kusto/kusto.py new file mode 100644 index 0000000..2434970 --- /dev/null +++ b/sigmaiq/backends/kusto/kusto.py @@ -0,0 +1,26 @@ +from sigma.backends.kusto import KustoBackend +from sigmaiq.backends.sigmaiq_abstract_backend import AbstractGenericSigmAIQBackendClass + + +class SigmAIQDefenderXDRBackend(AbstractGenericSigmAIQBackendClass, KustoBackend): + """SigmAIQ backend interface for the pySigma Kusto Backend library to translate a SigmaRule object + to a Kusto search query with the Microsoft Defender XDR format""" + custom_formats = {} + associated_pipelines = ["microsoft_xdr"] + default_pipeline = "microsoft_xdr" + + +class SigmAIQSentinelASIMBackend(AbstractGenericSigmAIQBackendClass, KustoBackend): + """SigmAIQ backend interface for the pySigma Kusto Backend library to translate a SigmaRule object + to a Kusto search query with the Microsoft Sentinel ASIM format""" + custom_formats = {} + associated_pipelines = ["sentinel_asim"] + default_pipeline = "sentinel_asim" + + +class SigmAIQAzureMonitorBackend(AbstractGenericSigmAIQBackendClass, KustoBackend): + """SigmAIQ backend interface for the pySigma Kusto Backend library to translate a SigmaRule object + to a Kusto search query with the Microsoft Azure Monitor format""" + custom_formats = {} + associated_pipelines = ["azure_monitor"] + default_pipeline = "azure_monitor" diff --git a/sigmaiq/backends/microsoft365defender/__init__.py b/sigmaiq/backends/microsoft365defender/__init__.py deleted file mode 100644 index 3f5b909..0000000 --- a/sigmaiq/backends/microsoft365defender/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .microsoft365defender import SigmAIQMicrosoft365DefenderBackend diff --git a/sigmaiq/backends/microsoft365defender/microsoft365defender.py b/sigmaiq/backends/microsoft365defender/microsoft365defender.py deleted file mode 100644 index fce82a9..0000000 --- a/sigmaiq/backends/microsoft365defender/microsoft365defender.py +++ /dev/null @@ -1,8 +0,0 @@ -from sigma.backends.microsoft365defender import Microsoft365DefenderBackend -from sigmaiq.backends.sigmaiq_abstract_backend import AbstractGenericSigmAIQBackendClass - - -class SigmAIQMicrosoft365DefenderBackend(AbstractGenericSigmAIQBackendClass, Microsoft365DefenderBackend): - custom_formats = {} - associated_pipelines = ["microsoft365defender"] - default_pipeline = "microsoft365defender" diff --git a/sigmaiq/backends/netwitness/__init__.py b/sigmaiq/backends/netwitness/__init__.py new file mode 100644 index 0000000..ca9281c --- /dev/null +++ b/sigmaiq/backends/netwitness/__init__.py @@ -0,0 +1 @@ +from .netwitness import SigmAIQNetwitnessBackend diff --git a/sigmaiq/backends/netwitness/netwitness.py b/sigmaiq/backends/netwitness/netwitness.py new file mode 100644 index 0000000..873c85d --- /dev/null +++ b/sigmaiq/backends/netwitness/netwitness.py @@ -0,0 +1,10 @@ +from sigma.backends.netwitness import NetWitnessBackend +from sigmaiq.backends.sigmaiq_abstract_backend import AbstractGenericSigmAIQBackendClass + + +class SigmAIQNetwitnessBackend(AbstractGenericSigmAIQBackendClass, NetWitnessBackend): + """SigmAIQ backend interface for the pySigma Netwitness Backend library to translate a SigmaRule object + to a Netwitness search query""" + + associated_pipelines = ["netwitness_windows"] + default_pipeline = "netwitness_windows" diff --git a/sigmaiq/globals.py b/sigmaiq/globals.py index 2f5a241..94e51ed 100644 --- a/sigmaiq/globals.py +++ b/sigmaiq/globals.py @@ -4,4 +4,4 @@ class DEFAULT_DIRS: ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) SIGMA_RULE_DIR = os.path.join(ROOT_DIR, "llm/data/sigma") - VECTOR_STORE_DIR = os.path.join(ROOT_DIR, "llm/data/vectordb") \ No newline at end of file + VECTOR_STORE_DIR = os.path.join(ROOT_DIR, "llm/data/vectordb") diff --git a/sigmaiq/llm/README.md b/sigmaiq/llm/README.md index 71885ea..1144a78 100644 --- a/sigmaiq/llm/README.md +++ b/sigmaiq/llm/README.md @@ -5,54 +5,91 @@

SigmAIQ: LLM

+# Table of Contents +- [Table of Contents](#table-of-contents) +- [Overview](#overview) +- [Features](#features) + - [Embedding Creation and Storage](#embedding-creation-and-storage) + - [Sigma Rule Similarity Searching](#sigma-rule-similarity-searching) + - [Agent/Bot for Sigma Rule Translation and Creation](#agentbot-for-sigma-rule-translation-and-creation) + - [Converting Backend Queries to Sigma Rules](#converting-backend-queries-to-sigma-rules) +- [Installation](#installation) +- [Usage](#usage) +- [Example Q\&A](#example-qa) +- [Known Issues](#known-issues) +- [TODO](#todo) +- [License](#license) +- [Contributing](#contributing) +- [Acknowledgements](#acknowledgements) -NOTE: This is an experimental feature that is under active development. It is not recommended for production use. -By default, OpenAI embeddings and LLM models (gpt-3.5-turbo) are used, which require an OpenAI API key set in the environmental -variable `OPENAI_API_KEY`. +# Overview -## Overview -The goal of this SigmAIQ feature is to utilize the power of LLMs and Vector Databases with Sigma Rules. -This feature uses [langchain](https://github.com/langchain-ai/langchain) and [pySigma](https://github.com/SigmaHQ/pySigma) -to utilize LLMs and Agents for Sigma Rule translation and creation. -Currently, the use cases of this feature include: -- Embedding creation and storage of Sigma Rules -- Sigma Rule similarity searching -- Agent/Bot for Sigma Rule translation and creation -- Converting backend queries to Sigma Rules +SigmAIQ LLM is an experimental feature that leverages the power of Large Language Models (LLMs) and Vector Databases to enhance Sigma Rule creation, translation, and searching. This feature integrates [langchain](https://github.com/langchain-ai/langchain) and [pySigma](https://github.com/SigmaHQ/pySigma) to provide advanced capabilities for working with Sigma Rules. -Please see the `examples` folder for use case examples. +**Note:** This feature is under active development and not recommended for production use. By default, it uses OpenAI embeddings and LLM models (gpt-4), which require an OpenAI API key set in the environmental variable `OPENAI_API_KEY`. -### Embedding Creation and Storage -The `sigmaiq.llm.base.SigmaLLM` class is used to automatically download the latest Sigma Rules from the [SigmaHQ](https://github.com/SigmaHQ/sigma/releases/latest) repo. -By default, this downloads the `sigma_core` ruleset into this projects `data` directory. Embeddings are then created for each rule and stored in a Vector Database. -By default, `OpenAIEmbeddings` and `FAISS` are used, respectively. The `sigmaiq.llm.base.SigmaLLM` class can be extended to use different embedding and vector database implementations. +# Features -### Sigma Rule Similarity Searching -The `sigmaiq.llm.base.SigmaLLM` class is also used to search for similar Sigma Rules using a similarity search. This does not require LLM models to be trained, as the embeddings are already created and stored in the Vector Database. -This can be a cheaper, yet less accurate option, for searching through Sigma Rules. By default, the top 3 matching rules -are returned based on the query sent to the similarity search. Other `langchain` `VectorStore` searching functionality can be used on the VectorStore as well. +1. **Embedding Creation and Storage**: Automatically download and create embeddings for the latest Sigma Rules. +2. **Sigma Rule Similarity Searching**: Efficiently search for similar Sigma Rules using vector similarity. +3. **Agent/Bot for Sigma Rule Translation and Creation**: Utilize LLM agents for advanced rule translation and creation. +4. **Converting Backend Queries to Sigma Rules**: Transform existing backend queries into Sigma Rules. -### Agent/Bot for Sigma Rule Translation and Creation -A `langchain` `Agent` can be created with the `create_sigma_agent()` function in `sigmaiq.llm.toolkits.base`. -This agent uses the tools contained in the `SigmaToolkit` class (in `sigmaiq/llm/toolkits/sigma_toolkit`) for various tasks. -The Agent will automatically determine what tools to use based on the query sent to it, and can run different tools in succession to complete a task. +## Embedding Creation and Storage +The `sigmaiq.llm.base.SigmaLLM` class automates the process of downloading the latest Sigma Rules from [SigmaHQ](https://github.com/SigmaHQ/sigma/releases/latest), creating embeddings, and storing them in a Vector Database. By default, it uses `OpenAIEmbeddings` and `FAISS`, but can be extended to use different implementations. -For rule translation, the Agent will automatically parse the contents of the user's query to determine what backend, pipeline, and output format -to use for the translation. The Agent will then create a `SigmAIQBackend` and translate the rule provided in the query. +## Sigma Rule Similarity Searching +Leverage the power of vector databases to find similar Sigma Rules quickly. This feature doesn't require LLM model training and can be a cost-effective option for rule searching. -For rule creation, the Agent will first look for similar Sigma Rules in the local Sigma VectorStore (from `SigmaLLM`) and return -the top 3 best matching rules. The Agent will then use these matching rules as context, in addition to the context/IOCs in the user's question, -to create a brand new Sigma Rule! The Agent will then return the newly created Sigma Rule to the user. +## Agent/Bot for Sigma Rule Translation and Creation +The `create_sigma_agent()` function in `sigmaiq.llm.toolkits.base` creates a `langchain` `Agent` that can: +- Automatically determine the appropriate backend, pipeline, and output format for rule translation. +- Create new Sigma Rules based on user queries and similar existing rules. +## Converting Backend Queries to Sigma Rules +Transform existing backend-specific queries into standardized Sigma Rules for better portability and management. -#### Example Q&A -This example demonstrates how the agent can use multiple tools in succession; in this case, a Sigma Rule is first created -based on the user's question with the rule creation tool, then the rule is translated to a Microsoft 365 Defender query with the rule translation tool. -The Sigma Rule YAML can be found retrieved in the `intermediate_steps` of the output. +# Installation -QUESTION: "Create a Windows process creation Sigma Rule for certutil downloading a file from definitely-not-malware.com, then translate it to a Microsoft 365 Defender query." +Clone this repository and install SigmAIQ dependencies along with the `llm` group: -ANSWER: +Using pip: +```bash +pip install -e . +pip install -r requirements/llm.txt +``` + +Using poetry: +```bash +poetry install --with llm +``` + +# Usage + +For detailed usage examples, please refer to the `examples` directory in the repository. Here's a basic example: + +```python +from sigmaiq.llm.base import SigmaLLM +from langchain_openai import OpenAIEmbeddings + +# Initialize SigmaLLM +sigma_llm = SigmaLLM(embedding_model=OpenAIEmbeddings(model="text-embedding-3-large")) + +# Create and save vector database +sigma_llm.create_sigma_vectordb(save=True) + +# Perform similarity search +query = "Encoded powershell commands" +results = sigma_llm.similarity_search(query) +``` + +# Example Q&A + +This example demonstrates how the agent can use multiple tools in succession: + +**Question:** "Create a Windows process creation Sigma Rule for certutil downloading a file from definitely-not-malware.com, then translate it to a Microsoft XDR query." + +**Answer:** Intermediate Step (Rule Creation): ```yaml @@ -75,61 +112,34 @@ falsepositives: level: high ``` -Final Output: - -Here is the translated Microsoft 365 Defender query: - +Final Output (Microsoft XDR KQL query): ``` DeviceProcessEvents | where FolderPath endswith "\\certutil.exe" and ProcessCommandLine contains "definitely-not-malware.com" ``` +# Known Issues -## Installation -Clone this repo, then install SigmAIQ dependencies along with the `llm` group dependencies -with your favorite Python package manager, such as pip or poetry. +- Agent parsing issues may occur when invalid JSON is passed between agent steps. -### pip -```bash -pip install -e . -pip install -r requirements/llm.txt -``` - -### poetry -```bash -poetry install --with llm -``` - - -## Usage -For usage examples, please see the `examples` directory. By default, OpenAI embeddings and LLM models are used, which -require an OpenAI API key set in the environmental variable `OPENAI_API_KEY`. +# TODO +- [ ] Add example for using custom (and free) embeddings and LLM models +- [ ] Add example for using custom Vector Databases +- [ ] Add ability to easily customize prompts for tools/agents +- [ ] Implement Sigma Rule Creation Tool without Vector Databases +- [ ] Add metadata to Vector Database entries for advanced filtering on Sigma Rule fields (e.g., category, product, level, status) -## Known Issues -- Agent parsing issues sometimes occur when invalid JSON is passed between agent steps. +# License - -## TODO -- Add example for using custom (and free) embeddings and LLM models -- Add example for using custom Vector Databases -- Add ability to easily customize prompts for tools/agents -- Sigma Rule Creation Tool without Vector Databases -- Adding metadata to Vector Database entries for advanced filtering on Sigma Rule fields - - I.E. category, product, level, status, etc - - -## License This project is licensed under the terms of the GNU LGPL, version 2.1. Please see the `LICENSE` file for full details. +# Contributing -## Contributing Contributions and use cases are welcome! Please submit a PR or issue if you would like to contribute or have any questions. +# Acknowledgements -## Acknowledgements -First and foremost, we'd like to acknowledge the creators, maintainers, contributors, and everyone else involved with the -[Sigma](https://github.com/SigmaHQ/sigma/) and [pySigma](https://github.com/SigmaHQ/pySigma) projects for obvious reasons. - -We'd also like to acknowledge the [langchain](https://github.com/langchain-ai/langchain) project the work with making -LLMs more accessible and easier to use. \ No newline at end of file +We'd like to acknowledge: +- The creators, maintainers, and contributors of the [Sigma](https://github.com/SigmaHQ/sigma/) and [pySigma](https://github.com/SigmaHQ/pySigma) projects. +- The [langchain](https://github.com/langchain-ai/langchain) project for making LLMs more accessible and easier to use. \ No newline at end of file diff --git a/sigmaiq/llm/base.py b/sigmaiq/llm/base.py index 2e083f7..c94de47 100644 --- a/sigmaiq/llm/base.py +++ b/sigmaiq/llm/base.py @@ -7,15 +7,16 @@ from sigmaiq.globals import DEFAULT_DIRS # langchain -from langchain.embeddings import OpenAIEmbeddings -from langchain.vectorstores import FAISS -from langchain.document_loaders import DirectoryLoader, TextLoader +from langchain_openai import OpenAIEmbeddings +from langchain_community.vectorstores import FAISS +from langchain_community.document_loaders import DirectoryLoader, TextLoader from langchain.text_splitter import CharacterTextSplitter # langchain typing from langchain.schema.embeddings import Embeddings from langchain.schema.vectorstore import VectorStore -from langchain.document_loaders.base import BaseLoader, Document +from langchain.docstore.document import Document +from langchain.document_loaders.base import BaseLoader from langchain.schema.document import BaseDocumentTransformer @@ -34,7 +35,8 @@ def __init__( self, rule_dir: str = None, vector_store_dir: str = None, - embedding_function: Type[Embeddings] = OpenAIEmbeddings, + embedding_model: OpenAIEmbeddings = None, + embedding_function: Type[Embeddings] = OpenAIEmbeddings, #TODO RS : Consolidate this with embedding_model vector_store: Type[VectorStore] = FAISS, rule_loader: Type[BaseLoader] = DirectoryLoader, rule_splitter: Type[BaseDocumentTransformer] = CharacterTextSplitter, @@ -60,7 +62,10 @@ def __init__( # Setup rest of class self.vector_store_dir = self._setup_vector_store_dir(vector_store_dir) - self.embedding_function = embedding_function() + if embedding_model: + self.embedding_function = embedding_model + else: + self.embedding_function = embedding_function() self.vector_store = vector_store self.sigmadb = None self.rule_loader = rule_loader @@ -75,6 +80,7 @@ def load_sigma_vectordb(self): self.sigmadb = self.vector_store.load_local( # CHANGE ME IF NEEDED folder_path=self.vector_store_dir, embeddings=self.embedding_function, + allow_dangerous_deserialization=True, ) except Exception as e: raise e diff --git a/sigmaiq/llm/toolkits/base.py b/sigmaiq/llm/toolkits/base.py index 770b3d2..6b04b5c 100644 --- a/sigmaiq/llm/toolkits/base.py +++ b/sigmaiq/llm/toolkits/base.py @@ -1,85 +1,58 @@ # stdlib -from typing import Optional, Dict, Any, Type, Union import json from json import JSONDecodeError +from typing import Optional, Dict, Any, Type, Union # langchain from langchain.agents.agent import AgentExecutor from langchain.agents.format_scratchpad import format_to_openai_function_messages from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser -from langchain.chat_models import ChatOpenAI from langchain.prompts import ChatPromptTemplate -from langchain.tools.render import format_tool_to_openai_function - # langchain typing -from langchain.schema import ( - AgentAction, - AgentFinish, - OutputParserException, -) -from langchain.schema.vectorstore import VectorStore -from langchain.schema.language_model import BaseLanguageModel +from langchain.schema import (AgentAction, AgentFinish, OutputParserException, AIMessage, BaseMessage) from langchain.schema.agent import AgentActionMessageLog -from langchain.schema.messages import ( - AIMessage, - BaseMessage, -) +from langchain.schema.language_model import BaseLanguageModel +from langchain.schema.messages import (AIMessage, BaseMessage, ) +from langchain.schema.vectorstore import VectorStore +from langchain_core.utils.function_calling import convert_to_openai_function +from langchain_openai import ChatOpenAI +from sigmaiq.llm.toolkits.prompts import SIGMA_AGENT_PROMPT # sigmaiq from sigmaiq.llm.toolkits.sigma_toolkit import SigmaToolkit -from sigmaiq.llm.toolkits.prompts import SIGMA_AGENT_PROMPT def create_sigma_agent( - agent_llm: BaseLanguageModel = ChatOpenAI(model="gpt-4o"), - rule_creation_llm: BaseLanguageModel = ChatOpenAI(model="gpt-4o"), - sigma_vectorstore: VectorStore = None, - toolkit: Type[SigmaToolkit] = SigmaToolkit, - prompt: Optional[ChatPromptTemplate] = SIGMA_AGENT_PROMPT, + sigma_vectorstore: Optional[VectorStore] = None, + rule_creation_llm: Optional[BaseLanguageModel] = None, verbose: bool = False, return_intermediate_steps: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, ) -> AgentExecutor: - """Construct a Sigma agent from an LLM and tools. - - Args: - agent_llm (BaseLanguageModel, optional): The LLM to use for the agent. Defaults to ChatOpenAI(model="gpt-4o"). - rule_creation_llm (BaseLanguageModel, optional): The LLM to use for the rule creation tool. Defaults to ChatOpenAI(model="gpt-4o"). - sigma_vectorstore (VectorStore, optional): The vectorstore containing Sigma rules to use for the agent. Defaults to None. - toolkit (Type[SigmaToolkit], optional): The toolkit to use for the agent. Defaults to SigmaToolkit. - prompt (Optional[ChatPromptTemplate], optional): The prompt to use for the agent. Defaults to SIGMA_AGENT_PROMPT. - verbose (bool, optional): Whether to print verbose output. Defaults to False. - return_intermediate_steps (bool, optional): Whether to return intermediate steps. Defaults to False. - agent_executor_kwargs (Optional[Dict[str, Any]], optional): Additional kwargs to pass to the AgentExecutor. Defaults to None. + if sigma_vectorstore is None: + raise ValueError("sigma_vectorstore must be provided") + + if rule_creation_llm is None: + rule_creation_llm = ChatOpenAI(model="gpt-4o") - Returns: - AgentExecutor: Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response - """ # noqa: E501 + toolkit = SigmaToolkit(sigmadb=sigma_vectorstore, rule_creation_llm=rule_creation_llm) + tools = toolkit.get_tools() - # Get Sigma Tools from the SigmaToolkit. Init with sigma vectorstore and rule creation llm - tools = toolkit(sigmadb=sigma_vectorstore, rule_creation_llm=rule_creation_llm).get_tools() + # Assert if any of the tools does not have arun + for tool in tools: + assert hasattr(tool, 'arun'), f"Tool {tool.name} does not have an 'arun' method" # Create OpenAI Function for each tool for the agent LLM, so we can create an OpenAI Function AgentExecutor - llm_with_tools = agent_llm.bind(functions=[format_tool_to_openai_function(t) for t in tools]) + llm_with_tools = rule_creation_llm.bind(functions=[convert_to_openai_function(t) for t in tools]) # Create the agent - agent = ( - { - "input": lambda x: x["input"], - "agent_scratchpad": lambda x: format_to_openai_function_messages(x["intermediate_steps"]), - } - | prompt - | llm_with_tools - | CustomOpenAIFunctionsAgentOutputParser() - ) + prompt = SIGMA_AGENT_PROMPT + agent = ({"input": lambda x: x["input"], "agent_scratchpad": lambda x: format_to_openai_function_messages( + x["intermediate_steps"]), } | prompt | llm_with_tools | CustomOpenAIFunctionsAgentOutputParser()) # Create and return the AgentExecutor - agent_executor = AgentExecutor( - agent=agent, - tools=tools, - verbose=verbose, - return_intermediate_steps=return_intermediate_steps, - handle_parsing_errors=True, + agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=verbose, + return_intermediate_steps=return_intermediate_steps, handle_parsing_errors=True, **(agent_executor_kwargs or {})) return agent_executor @@ -88,15 +61,21 @@ def create_sigma_agent( class CustomOpenAIFunctionsAgentOutputParser(OpenAIFunctionsAgentOutputParser): """Custom OpenAIFunctionsAgentOutputParser to overcome the JSON parsing error on some agent intermediate step inputs. This occurs because the `json.load()` method needs the arg `strict=False` to -parse the JSON. This is a hacky way to do this, but it works for now. + parse the JSON. This is a hacky way to do this, but it works for now. """ - # Override - @staticmethod - def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]: + + def parse(self, message: Union[str, BaseMessage]) -> Union[AgentAction, AgentFinish]: """Parse an AI message.""" + if isinstance(message, str): + raise ValueError("Expected an AIMessage object, got a string") if not isinstance(message, AIMessage): raise TypeError(f"Expected an AI message got {type(message)}") + + return self._parse_ai_message(message) + @staticmethod + def _parse_ai_message(message: AIMessage) -> Union[AgentAction, AgentFinish]: + """Parse an AI message.""" function_call = message.additional_kwargs.get("function_call", {}) if function_call: @@ -104,10 +83,8 @@ def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]: try: _tool_input = json.loads(function_call["arguments"].strip(), strict=False) # HACK except JSONDecodeError: - raise OutputParserException( - f"Could not parse tool input: {function_call} because " - f"the `arguments` is not valid JSON." - ) + raise OutputParserException(f"Could not parse tool input: {function_call} because " + f"the `arguments` is not valid JSON.") # HACK HACK HACK: # The code that encodes tool input into Open AI uses a special variable @@ -122,13 +99,6 @@ def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]: content_msg = f"responded: {message.content}\n" if message.content else "\n" log = f"\nInvoking: `{function_name}` with `{tool_input}`\n{content_msg}\n" - return AgentActionMessageLog( - tool=function_name, - tool_input=tool_input, - log=log, - message_log=[message], - ) - - return AgentFinish( - return_values={"output": message.content}, log=str(message.content) - ) + return AgentActionMessageLog(tool=function_name, tool_input=tool_input, log=log, message_log=[message]) + + return AgentFinish(return_values={"output": message.content}, log=str(message.content)) diff --git a/sigmaiq/llm/tools/create_sigma_rule.py b/sigmaiq/llm/tools/create_sigma_rule.py index 6df39bd..304bdfb 100644 --- a/sigmaiq/llm/tools/create_sigma_rule.py +++ b/sigmaiq/llm/tools/create_sigma_rule.py @@ -1,17 +1,17 @@ # stdlib -from typing import Type, Optional +import asyncio +from typing import Type # langchain -from langchain.callbacks.manager import CallbackManagerForToolRun from langchain.prompts import ChatPromptTemplate -from langchain.tools import BaseTool from langchain.pydantic_v1 import BaseModel, Field # langchain typing from langchain.schema.language_model import BaseLanguageModel -from langchain.schema.runnable import RunnablePassthrough from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough from langchain.schema.vectorstore import VectorStore +from langchain.tools import BaseTool class CreateSigmaRuleInput(BaseModel): @@ -32,99 +32,90 @@ class CreateSigmaRuleVectorStoreTool(BaseTool): name: str = "create_sigma_rule_vectorstore" args_schema: Type[BaseModel] = CreateSigmaRuleInput description: str = """Use this tool to take the users input, find similar Sigma Rules from the vectorstore, - then create a brand new Sigma Rule based on the users input and the similar Sigma Rules returned from the vectorstore - to use as context. The output is a Sigma Rule in YAML format. - """ +then create a brand new Sigma Rule based on the users input and the similar Sigma Rules returned from the vectorstore +to use as context. The output is a Sigma Rule in YAML format. +""" sigmadb: VectorStore llm: BaseLanguageModel k: int = 3 verbose = False + verbose = False - def _run( - self, - query: str, - run_manager: Optional[CallbackManagerForToolRun] = None, - ) -> str: + def _run(self, query: str) -> str: """Run the tool""" + return asyncio.run(self._arun(query)) + async def _arun(self, query: str) -> str: + """Async run the tool""" template = """You are a cybersecurity detection engineering assistant bot specializing in Sigma Rule creation. - You are assisting a user in creating a new Sigma Rule based on the users question. - The users question is first used to find similar Sigma Rules from the a vectorstore containing official - Sigma Rules. The official Sigma Rules can be used as context as needed in conjunction with the detection specified - in the users question to create a new Sigma Rule. - The created Sigma Rule should be in YAML format and use the official Sigma schema. The detection field - can contain multiple 'selection' identifiers and multiple 'filter' identifiers as needed, - which can be used in the condition field to select criteria and filter out criteria respectively. - Set the 'author' to 'SigmAIQ (AttackIQ)', the date to today's date, and the reference to 'https://github.com/AttackIQ/SigmAIQ'. - If you use other rules as context and derive the created Sigma Rules from the context rules, you must - include the original authors under the 'author' field in the new rule in addition to "SigmAIQ (AttackIQ), - and add the original rule IDs under the 'related' field. The valid 'types' under 'related' are the following: - - derived: The rule was derived from the referred rule or rules, which may remain active. - obsoletes: The rule obsoletes the referred rule or rules, which aren't used anymore. - merged: The rule was merged from the referred rules. The rules may be still existing and in use. - renamed: The rule had previously the referred identifier or identifiers but was renamed for whatever reason, e.g. from a private naming scheme to UUIDs, to resolve collisions etc. It's not expected that a rule with this id exists anymore. - similar: Use to relate similar rules to each other (e.g. same detection content applied to different log sources, rule that is a modified version of another rule with a different level) - - If you are unsure about the Sigma rule schema, you can get the information from the official - Sigma specification here first: https://raw.githubusercontent.com/SigmaHQ/sigma-specification/main/Sigma_specification.md - - ------------ - - Sigma Rule Schema: - - title - id [optional] - related [optional] - - id {{rule-id}} - type {{type-identifier}} - status [optional] - description [optional] - references [optional] - author [optional] - date [optional] - modified [optional] - tags [optional] - logsource - category [optional] - product [optional] - service [optional] - definition [optional] - ... - detection - {{search-identifier}} [optional] - {{string-list}} [optional] - {{map-list}} [optional] - {{field: value}}> [optional] - ... # Multiple search identifiers can be specified as needed and used in the condition - condition - fields [optional] - falsepositives [optional] - level [optional]: - - - ------------ - - Vectorstore Search Results: - - {context} - - ------------ - - User's Question: - {question} - """ +You are assisting a user in creating a new Sigma Rule based on the users question. +The users question is first used to find similar Sigma Rules from the a vectorstore containing official +Sigma Rules. The official Sigma Rules can be used as context as needed in conjunction with the detection specified +in the users question to create a new Sigma Rule. +The created Sigma Rule should be in YAML format and use the official Sigma schema. The detection field +can contain multiple 'selection' identifiers and multiple 'filter' identifiers as needed, +which can be used in the condition field to select criteria and filter out criteria respectively. +Set the 'author' to 'SigmAIQ (AttackIQ)', the date to today's date, and the reference to 'https://github.com/AttackIQ/SigmAIQ'. +If you use other rules as context and derive the created Sigma Rules from the context rules, you must +include the original authors under the 'author' field in the new rule in addition to "SigmAIQ (AttackIQ), +and add the original rule IDs under the 'related' field. The valid 'types' under 'related' are the following: + + derived: The rule was derived from the referred rule or rules, which may remain active. + obsoletes: The rule obsoletes the referred rule or rules, which aren't used anymore. + merged: The rule was merged from the referred rules. The rules may be still existing and in use. + renamed: The rule had previously the referred identifier or identifiers but was renamed for whatever reason, e.g. from a private naming scheme to UUIDs, to resolve collisions etc. It's not expected that a rule with this id exists anymore. + similar: Use to relate similar rules to each other (e.g. same detection content applied to different log sources, rule that is a modified version of another rule with a different level) + +If you are unsure about the Sigma rule schema, you can get the information from the official +Sigma specification here first: https://raw.githubusercontent.com/SigmaHQ/sigma-specification/main/Sigma_specification.md + +------------ + +Sigma Rule Schema: + +title +id [optional] +related [optional] + - id {{rule-id}} + type {{type-identifier}} +status [optional] +description [optional] +references [optional] +author [optional] +date [optional] +modified [optional] +tags [optional] +logsource + category [optional] + product [optional] + service [optional] + definition [optional] + ... +detection + {{search-identifier}} [optional] + {{string-list}} [optional] + {{map-list}} [optional] + {{field: value}}> [optional] + ... # Multiple search identifiers can be specified as needed and used in the condition + condition +fields [optional] +falsepositives [optional] +level [optional]: + + +------------ + +Vectorstore Search Results: + +{context} + +------------ + +User's Question: +{query} +""" prompt = ChatPromptTemplate.from_template(template) retriever = self.sigmadb.as_retriever(search_kwargs={"k": self.k}) - chain = {"context": retriever, "question": RunnablePassthrough()} | prompt | self.llm | StrOutputParser() - return chain.invoke(query) - - async def _arun( - self, - query: str, - k: int, - run_manager: Optional[CallbackManagerForToolRun] = None, - ) -> str: - """Async run the tool""" - raise NotImplementedError + chain = {"context": retriever, "query": RunnablePassthrough()} | prompt | self.llm | StrOutputParser() + return await chain.ainvoke(query) diff --git a/sigmaiq/llm/tools/find_sigma_rule.py b/sigmaiq/llm/tools/find_sigma_rule.py index 7b1162a..6066e1d 100644 --- a/sigmaiq/llm/tools/find_sigma_rule.py +++ b/sigmaiq/llm/tools/find_sigma_rule.py @@ -1,6 +1,6 @@ -import json -from typing import Union, Type, Optional -from langchain.callbacks.manager import CallbackManagerForToolRun +import asyncio +from typing import Union, Type + from langchain.prompts import ChatPromptTemplate from langchain.schema.language_model import BaseLanguageModel from langchain.schema.output_parser import StrOutputParser @@ -10,15 +10,12 @@ from pydantic import BaseModel, Field, Extra - class FindSigmaRuleInput(BaseModel): - """Input for QueryToSigmaRule tool, which converts a backend query to a Sigma Rule, and - uses SigmAIQ backend factory for validation. - """ + """Input for FindSigmaRule tool, which searches for Sigma Rules in a vector database.""" query: Union[str, dict] = Field( - default=None, - description="""A query string for a backend, which should be converted to a Sigma Rule YAML string.""" + ..., # This makes the field required + description="""A query string or dictionary to search for Sigma Rules in the vector database.""", ) @@ -28,51 +25,40 @@ class FindSigmaRuleTool(BaseTool): name: str = "find_sigma_rule" args_schema: Type[BaseModel] = FindSigmaRuleInput description: str = """ - Use this tool to search for a Sigma Rule in the vector database. The input should be relevent information, such as - log artifacts, event IDs, operating systems, categories, indicators of compromise, MITRE ATT&CK information, or other relevant information to use - to search the vector store. If multiple rules are returned from the vector store, select the most similar Sigma Rule and return it in YAML format. - """ - # return_direct = True # We don't need an agent LLM to think about the output, it is what it is. +Use this tool to search for a Sigma Rule in the vector database. The input should be relevant information, such as +log artifacts, event IDs, operating systems, categories, indicators of compromise, MITRE ATT&CK information, or other relevant information to use +to search the vector store. If multiple rules are returned from the vector store, select the most similar Sigma Rule and return it in YAML format. +""" llm: BaseLanguageModel sigmadb: VectorStore k: int = 3 - verbose = False + verbose: bool = False class Config: """Configuration for this pydantic object.""" - extra = Extra.forbid - def _run( - self, - query: str = None, - run_manager: Optional[CallbackManagerForToolRun] = None, - ) -> str: + def _run(self, query: Union[str, dict]) -> str: + return asyncio.run(self._arun(query)) + + async def _arun(self, query: Union[str, dict]) -> str: template = """You are a cybersecurity detection engineering assistant bot specializing in Sigma Rules. - You are assisting a user searching for Sigma Rules stored in a vectorstore. - Based on the users question, extract the relevent information, such as - log artifacts, event IDs, operating systems, categories, indicators of compromise, - MITRE ATT&CK information, or other relevant information to use - to search the vector store. If multiple rules are returned from the - vector store, select the most similar Sigma Rule and return it in YAML format. Output the entire rule. - ------- - Vectorstore Search Results: - - {context} - ------ - User's Question: - {question} - """ +You are assisting a user searching for Sigma Rules stored in a vectorstore. +Based on the user's question, extract the relevant information, such as +log artifacts, event IDs, operating systems, categories, indicators of compromise, +MITRE ATT&CK information, or other relevant information to use +to search the vector store. If multiple rules are returned from the +vector store, select the most similar Sigma Rule and return it in YAML format. Output the entire rule. +------- +Vectorstore Search Results: + +{context} +------ +User's Question: +{question} +""" prompt = ChatPromptTemplate.from_template(template) retriever = self.sigmadb.as_retriever(search_kwargs={"k": self.k}) chain = {"context": retriever, "question": RunnablePassthrough()} | prompt | self.llm | StrOutputParser() - return chain.invoke(query) - - async def _arun( - self, - query: Union[str, dict] = None, - run_manager: Optional[CallbackManagerForToolRun] = None, - ) -> str: - """Async run the tool""" - raise NotImplementedError + return await chain.ainvoke(query) diff --git a/sigmaiq/llm/tools/query_to_sigma_rule.py b/sigmaiq/llm/tools/query_to_sigma_rule.py index 637ca8c..1762377 100644 --- a/sigmaiq/llm/tools/query_to_sigma_rule.py +++ b/sigmaiq/llm/tools/query_to_sigma_rule.py @@ -1,7 +1,7 @@ +import asyncio import json -from typing import Union, Type, Optional +from typing import Union, Type -from langchain.callbacks.manager import CallbackManagerForToolRun from langchain.prompts import ChatPromptTemplate from langchain.schema.language_model import BaseLanguageModel from langchain.schema.output_parser import StrOutputParser @@ -9,7 +9,7 @@ from langchain.tools import BaseTool from pydantic import BaseModel, Field, Extra -from sigmaiq.sigmaiq_backend_factory import AVAILABLE_BACKENDS, SigmAIQBackend +from sigmaiq.sigmaiq_backend_factory import AVAILABLE_BACKENDS class QueryToSigmaRuleInput(BaseModel): @@ -19,7 +19,7 @@ class QueryToSigmaRuleInput(BaseModel): query: Union[str, dict] = Field( default=None, - description="""A query string for a backend, which should be converted to a Sigma Rule YAML string.""" + description="""A query string for a backend, which should be converted to a Sigma Rule YAML string.""", ) backend: str = Field( default=None, @@ -35,12 +35,12 @@ class QueryToSigmaRuleTool(BaseTool): name: str = "query_to_sigma_rule" args_schema: Type[BaseModel] = QueryToSigmaRuleInput description: str = """ - Use this tool to take an already existing query for a backend and convert it to a Sigma Rule. - Use the translate_sigma_rule tool to take the created Sigma Rule and convert it back to a query to - determine if the queries are the same and the conversion is successful. Fix any errors if necessary. - The input must be a query string for a backend, and the backend must be specified. - The output is a Sigma Rule YAML string, or an error message if the conversion fails. - """ +Use this tool to take an already existing query for a backend and convert it to a Sigma Rule. +Use the translate_sigma_rule tool to take the created Sigma Rule and convert it back to a query to +determine if the queries are the same and the conversion is successful. Fix any errors if necessary. +The input must be a query string for a backend, and the backend must be specified. +The output is a Sigma Rule YAML string, or an error message if the conversion fails. +""" # return_direct = True # We don't need an agent LLM to think about the output, it is what it is. llm: BaseLanguageModel verbose = False @@ -50,70 +50,59 @@ class Config: extra = Extra.forbid - def _run( - self, - query: str = None, - backend: str = None, - run_manager: Optional[CallbackManagerForToolRun] = None, - ) -> str: + def _run(self, query: str = None, backend: str = None) -> str: + return asyncio.run(self._arun(query, backend)) + + async def _arun(self, query: str = None, backend: str = None) -> str: template = """You are a cybersecurity detection engineering assistant bot specializing in Sigma Rule creation. - You are assisting a user in taking a query for a security/SIEM product, and converting it to a Sigma Rule. - The backend is used to validate the query and ensure it is compatible with the backend. - The created Sigma Rule should be in YAML format and use the official Sigma schema. The detection field - can contain multiple 'selection' identifiers and multiple 'filter' identifiers as needed, - which can be used in the condition field to select criteria and filter out criteria respectively. - The fields should be Sysmon field names if possible, or Windows Event Log field names if possible. - - ----------- - - Sigma Rule Schema: - - title - id [optional] - related [optional] - - id {{rule-id}} - type {{type-identifier}} - status [optional] - description [optional] - references [optional] - author [optional] - date [optional] - modified [optional] - tags [optional] - logsource - category [optional] - product [optional] - service [optional] - definition [optional] - ... - detection - {{search-identifier}} [optional] - {{string-list}} [optional] - {{map-list}} [optional] - {{field: valu}}> [optional] - ... # Multiple search identifiers can be specified as needed and used in the condition - condition - fields [optional] - falsepositives [optional] - level [optional]: - - ----------- - - User's Question: - {question} - """ +You are assisting a user in taking a query for a security/SIEM product, and converting it to a Sigma Rule. +The backend is used to validate the query and ensure it is compatible with the backend. +The created Sigma Rule should be in YAML format and use the official Sigma schema. The detection field +can contain multiple 'selection' identifiers and multiple 'filter' identifiers as needed, +which can be used in the condition field to select criteria and filter out criteria respectively. +The fields should be Sysmon field names if possible, or Windows Event Log field names if possible. + +----------- + +Sigma Rule Schema: + +title +id [optional] +related [optional] + - id {{rule-id}} + type {{type-identifier}} +status [optional] +description [optional] +references [optional] +author [optional] +date [optional] +modified [optional] +tags [optional] +logsource + category [optional] + product [optional] + service [optional] + definition [optional] + ... +detection + {{search-identifier}} [optional] + {{string-list}} [optional] + {{map-list}} [optional] + {{field: valu}}> [optional] + ... # Multiple search identifiers can be specified as needed and used in the condition + condition +fields [optional] +falsepositives [optional] +level [optional]: + +----------- + +User's Query: {query} +Backend: {backend} +""" prompt = ChatPromptTemplate.from_template(template) - chain = {"question": RunnablePassthrough()} | prompt | self.llm | StrOutputParser() - return chain.invoke(query) - - async def _arun( - self, - sigma_rule: Union[str, dict] = None, - backend: str = None, - processing_pipeline: str = None, - output_format: str = "default", - run_manager: Optional[CallbackManagerForToolRun] = None, - ) -> str: - """Async run the tool""" - raise NotImplementedError + chain = ( + {"query": RunnablePassthrough(), "backend": RunnablePassthrough()} | prompt | self.llm | StrOutputParser() + ) + return await chain.ainvoke({"query": query, "backend": backend}) diff --git a/sigmaiq/llm/tools/translate_sigma_rule.py b/sigmaiq/llm/tools/translate_sigma_rule.py index 9fc1682..d06f06a 100644 --- a/sigmaiq/llm/tools/translate_sigma_rule.py +++ b/sigmaiq/llm/tools/translate_sigma_rule.py @@ -1,10 +1,12 @@ -from langchain.callbacks.manager import CallbackManagerForToolRun +import asyncio +import json +from typing import Union, Type + from langchain.tools import BaseTool from pydantic import BaseModel, Field, Extra -from typing import Union, Type, Optional + from sigmaiq.sigmaiq_backend_factory import AVAILABLE_BACKENDS, SigmAIQBackend from sigmaiq.sigmaiq_pipeline_factory import AVAILABLE_PIPELINES -import json class TranslateSigmaRuleInput(BaseModel): @@ -14,30 +16,30 @@ class TranslateSigmaRuleInput(BaseModel): sigma_rule: Union[str, dict] = Field( default=None, description="""The Sigma Rule to translate. This can be one of two formats: - - 1. A YAML string of the Sigma rule, with at least the title, logsource, and detection fields. - 2. A dict object of the Sigma rule, which is the same as the YAML string and must contain the - "title, logsource, and detection fields""", + +1. A YAML string of the Sigma rule, with at least the title, logsource, and detection fields. +2. A dict object of the Sigma rule, which is the same as the YAML string and must contain the +"title, logsource, and detection fields""", ) backend: str = Field( default=None, description="""The backend or product to translate the Sigma rule to. Backend options their descriptions are as - follows:\n""" +follows:\n""" + f"{json.dumps(AVAILABLE_BACKENDS, indent=2)}", ) processing_pipeline: str = Field( default=None, description="""The processing pipeline to use for the Sigma rule. This should only be set if explicitly provided - by the user, as certain pipelines are only compatible with certain backends. Otherwise, set this to None. - Pipeline options and their - descriptions are as follows:\n""" +by the user, as certain pipelines are only compatible with certain backends. Otherwise, set this to None. +Pipeline options and their +descriptions are as follows:\n""" + f"{json.dumps({k: v['description'] for k, v in AVAILABLE_PIPELINES.items()}, indent=2)}", ) output_format: str = Field( default="default", description="""The output format for the translated rule. Unless specified, 'default' should be used, as this is - the option available in all backends. Each backend option and valid backends with their - descriptions are as follows:\n""" + the option available in all backends. Each backend option and valid backends with their + descriptions are as follows:\n""" + f"{json.dumps({k: v['output_formats'] for k, v in SigmAIQBackend.display_backends_and_outputs().items()}, indent=2)}", ) @@ -48,13 +50,13 @@ class TranslateSigmaRuleTool(BaseTool): name: str = "translate_sigma_rule" args_schema: Type[BaseModel] = TranslateSigmaRuleInput description: str = """ - Use this tool to translate or convert a Sigma rule into a query for a specific backend. - The input must be a Sigma Rule, which can be provided as a YAML string or dict object. - Additionally, the backend (product) must be specified, and the processing pipeline and output format can be - optionally specified. - The output is json of the translated rule to a query for the backend, or an error message if the - translation fails. - """ +Use this tool to translate or convert a Sigma rule into a query for a specific backend. +The input must be a Sigma Rule, which can be provided as a YAML string or dict object. +Additionally, the backend (product) must be specified, and the processing pipeline and output format can be +optionally specified. +The output is json of the translated rule to a query for the backend, or an error message if the +translation fails. +""" # return_direct = True # We don't need an agent LLM to think about the output, it is what it is. verbose = False @@ -69,8 +71,18 @@ def _run( backend: str = None, processing_pipeline: str = None, output_format: str = "default", - run_manager: Optional[CallbackManagerForToolRun] = None, ) -> str: + """Run the tool""" + return asyncio.run(self._arun(sigma_rule, backend, processing_pipeline, output_format)) + + async def _arun( + self, + sigma_rule: Union[str, dict] = None, + backend: str = None, + processing_pipeline: str = None, + output_format: str = "default", + ) -> str: + """Async run the tool""" # Get backend object backend_obj = SigmAIQBackend( backend=backend, processing_pipeline=processing_pipeline, output_format=output_format @@ -82,14 +94,3 @@ def _run( output = f"ERROR: {e}" # Return translated rule return output - - async def _arun( - self, - sigma_rule: Union[str, dict] = None, - backend: str = None, - processing_pipeline: str = None, - output_format: str = "default", - run_manager: Optional[CallbackManagerForToolRun] = None, - ) -> str: - """Async run the tool""" - raise NotImplementedError diff --git a/sigmaiq/sigmaiq_backend_factory.py b/sigmaiq/sigmaiq_backend_factory.py index e64757c..a25a7ff 100644 --- a/sigmaiq/sigmaiq_backend_factory.py +++ b/sigmaiq/sigmaiq_backend_factory.py @@ -1,42 +1,50 @@ import logging from copy import deepcopy -from typing import Union, Dict, Any, Optional, List +from typing import Any, Dict, List, Optional, Union from sigma.collection import SigmaCollection from sigma.processing.pipeline import ProcessingPipeline from sigma.rule import SigmaRule -from sigmaiq.exceptions import InvalidSigmAIQBackend -from sigmaiq.sigmaiq_pipeline_factory import SigmAIQPipelineResolver, SigmAIQPipeline -from sigmaiq.utils.sigmaiq.sigmaiq_utils import create_sigma_rule_obj - # Backends from sigmaiq.backends.carbonblack import SigmAIQCarbonBlackBackend -from sigmaiq.backends.crowdstrike import SigmAIQCrowdstrikeSplunkBackend from sigmaiq.backends.cortexxdr import SigmAIQCortexXDRBackend +from sigmaiq.backends.crowdstrike import ( + SigmAIQCrowdstrikeLogscaleBackend, + SigmAIQCrowdstrikeSplunkBackend, +) from sigmaiq.backends.elasticsearch import SigmAIQElasticsearchBackend from sigmaiq.backends.insightidr import SigmAIQInsightIDRBackend +from sigmaiq.backends.kusto import SigmAIQDefenderXDRBackend, SigmAIQSentinelASIMBackend, SigmAIQAzureMonitorBackend from sigmaiq.backends.loki import SigmAIQLokiBackend -from sigmaiq.backends.microsoft365defender import SigmAIQMicrosoft365DefenderBackend +from sigmaiq.backends.netwitness import SigmAIQNetwitnessBackend from sigmaiq.backends.opensearch import SigmAIQOpensearchBackend from sigmaiq.backends.qradar import SigmAIQQRadarBackend from sigmaiq.backends.sentinelone import SigmAIQSentinelOneBackend -from sigmaiq.backends.splunk import SigmAIQSplunkBackend from sigmaiq.backends.sigma import SigmAIQSigmaBackend -from sigmaiq.backends.stix import SigmAIQStixBackend ## Abstract from sigmaiq.backends.sigmaiq_abstract_backend import AbstractGenericSigmAIQBackendClass +from sigmaiq.backends.splunk import SigmAIQSplunkBackend +from sigmaiq.backends.stix import SigmAIQStixBackend +from sigmaiq.exceptions import InvalidSigmAIQBackend +from sigmaiq.sigmaiq_pipeline_factory import SigmAIQPipeline, SigmAIQPipelineResolver +# Utils +from sigmaiq.utils.sigmaiq.sigmaiq_utils import create_sigma_rule_obj AVAILABLE_BACKENDS = { "carbonblack": "Carbon Black EDR", "cortexxdr": "Palo Alto Cortex XDR", - "crowdstrike_splunk": "Crowdstrike Splunk Query", + "crowdstrike_splunk": "Crowdstrike FDR Splunk Query", + "crowdstrike_logscale": "Crowdstrike Logscale Query", "elasticsearch": "Elastic Elasticsearch SIEM", "insightidr": "Rapid7 InsightIDR SIEM", "loki": "Grafana Loki LogQL SIEM", - "microsoft365defender": "Microsoft 365 Defender Advanced Hunting Query (KQL)", + "microsoft_xdr": "Microsoft XDR Advanced Hunting Query (KQL) (Defender, Office365, etc)", + "microsoft_sentinel_asim": "Microsoft Sentinel ASIM Query (KQL)", + "microsoft_azure_monitor": "Microsoft Azure Monitor Query (KQL)", + "netwitness": "Netwitness Query", "opensearch": "OpenSearch Lucene", "qradar": "IBM QRadar", "sentinelone": "SentinelOne EDR", @@ -91,9 +99,14 @@ def create_backend(self) -> AbstractGenericSigmAIQBackendClass: return SigmAIQCortexXDRBackend(**kwargs) # Crowdstrike Splunk Query if self.backend == "crowdstrike_splunk": - pipelines = ["crowdstrike", kwargs["processing_pipeline"]] + pipelines = ["crowdstrike_fdr", kwargs["processing_pipeline"]] kwargs["processing_pipeline"] = SigmAIQPipelineResolver(pipelines).process_pipelines() return SigmAIQCrowdstrikeSplunkBackend(**kwargs) + # Crowdstrike Logscale Query + if self.backend == "crowdstrike_logscale": + pipelines = ["crowdstrike_falcon", kwargs["processing_pipeline"]] + kwargs["processing_pipeline"] = SigmAIQPipelineResolver(pipelines).process_pipelines() + return SigmAIQCrowdstrikeLogscaleBackend(**kwargs) # Elasticsearch if self.backend == "elasticsearch": return SigmAIQElasticsearchBackend(**kwargs) @@ -103,9 +116,16 @@ def create_backend(self) -> AbstractGenericSigmAIQBackendClass: # Loki (Grafana) if self.backend == "loki": return SigmAIQLokiBackend(**kwargs) - # Microsoft 365 Defender - if self.backend == "microsoft365defender": - return SigmAIQMicrosoft365DefenderBackend(**kwargs) + # Microsoft Kusto + if self.backend == "microsoft_xdr": + return SigmAIQDefenderXDRBackend(**kwargs) + if self.backend == "microsoft_sentinel_asim": + return SigmAIQSentinelASIMBackend(**kwargs) + if self.backend == "microsoft_azure_monitor": + return SigmAIQAzureMonitorBackend(**kwargs) + # Netwitness + if self.backend == "netwitness": + return SigmAIQNetwitnessBackend(**kwargs) # Opensearch if self.backend == "opensearch": return SigmAIQOpensearchBackend(**kwargs) diff --git a/sigmaiq/sigmaiq_pipeline_factory.py b/sigmaiq/sigmaiq_pipeline_factory.py index 6b402c5..526284e 100644 --- a/sigmaiq/sigmaiq_pipeline_factory.py +++ b/sigmaiq/sigmaiq_pipeline_factory.py @@ -21,10 +21,17 @@ from sigma.pipelines.cortexxdr import CortexXDR_pipeline ## Crowdstrike -from sigma.pipelines.crowdstrike import crowdstrike_fdr_pipeline +from sigma.pipelines.crowdstrike import crowdstrike_fdr_pipeline, crowdstrike_falcon_pipeline ## Elasticsearch -from sigma.pipelines.elasticsearch import ecs_windows, ecs_windows_old, ecs_zeek_beats, ecs_zeek_corelight, zeek_raw +from sigma.pipelines.elasticsearch import ( + ecs_windows, + ecs_windows_old, + ecs_zeek_beats, + ecs_zeek_corelight, + ecs_kubernetes, + zeek_raw, +) ## InsightIDR from sigma.pipelines.insight_idr import insight_idr_pipeline @@ -32,8 +39,13 @@ ## Loki from sigma.pipelines.loki import loki_grafana_logfmt, loki_promtail_sysmon, loki_okta_system_log -## Microsoft 365 Defender -from sigma.pipelines.microsoft365defender import microsoft_365_defender_pipeline +## Microsoft +from sigma.pipelines.microsoftxdr import microsoft_xdr_pipeline +from sigma.pipelines.sentinelasim import sentinel_asim_pipeline +from sigma.pipelines.azuremonitor import azure_monitor_pipeline + +# Netwitness +from sigma.pipelines.netwitness import netwitness_windows_pipeline ## QRadar from sigma.pipelines.QRadarAQL import QRadarAQL_fields_pipeline, QRadarAQL_payload_pipeline @@ -84,34 +96,44 @@ "display_name": "CB", }, # Crowdstrike - "crowdstrike": { + "crowdstrike_fdr": { "description": "Crowdstrike FDR Splunk Mappings", "pipeline": crowdstrike_fdr_pipeline(), - "display_name": "CrowdStrike SPL", + "display_name": "CrowdStrike FDR SPL", + }, + "crowdstrike_falcon": { + "description": "Crowdstrike Falcon Logscale Mappings", + "pipeline": crowdstrike_falcon_pipeline(), + "display_name": "CrowdStrike Falcon Logscale", + }, + # Elasticsearch + "ecs_kubernetes": { + "description": "Elastic Common Schema (ECS) Kubernetes audit log mappings", + "pipeline": ecs_kubernetes(), + "display_name": "ECS Kubernetes", }, - # Elastic "ecs_windows": { - "description": "ECS mapping for Windows event logs ingested with Winlogbeat", + "description": "Elastic Common Schema (ECS) Windows log mappings from Winlogbeat from version 7", "pipeline": ecs_windows(), "display_name": "ECS Winlogbeat", }, "ecs_windows_old": { - "description": "ECS mapping for Windows event logs ingested with Winlogbeat <= 6.x", + "description": "Elastic Common Schema (ECS) Windows log mappings from Winlogbeat up to version 6", "pipeline": ecs_windows_old(), "display_name": "ESC Winlogbeat (<= v6.x)", }, "ecs_zeek_beats": { - "description": "Zeek ECS mapping from Elastic", + "description": "Elastic Common Schema (ECS) for Zeek using filebeat >= 7.6.1", "pipeline": ecs_zeek_beats(), "display_name": "ECS Zeek (Elastic)", }, "ecs_zeek_corelight": { - "description": "Zeek ECS mapping from Corelight", + "description": "Elastic Common Schema (ECS) mapping from Corelight", "pipeline": ecs_zeek_corelight(), "display_name": "ESC Zeek (Corelight)", }, "zeek_raw": { - "description": "Zeek raw JSON log field naming", + "description": "Zeek raw JSON field naming", "pipeline": zeek_raw(), "display_name": "Zeek Raw JSON", }, @@ -137,11 +159,29 @@ "pipeline": loki_okta_system_log(), "display_name": "Okta System Event", }, - # Microsoft 365 Defender - "microsoft365defender": { - "description": "Mappings for Sysmon -> Advanced Hunting Query Table Schema", - "pipeline": microsoft_365_defender_pipeline(), - "display_name": "KustoQL", + # Microsoft Kusto + "microsoft_xdr": { + "description": "Mappings for Sysmon -> XDR Advanced Hunting Query Table Schema", + "pipeline": microsoft_xdr_pipeline(), + "display_name": "Microsoft XDR KustoQL", + }, + # Microsoft Sentinel ASIM + "sentinel_asim": { + "description": "Mappings for Sysmon -> Sentinel ASIM Query Table Schema", + "pipeline": sentinel_asim_pipeline(), + "display_name": "Sentinel ASIM KustoQL", + }, + # Microsoft Azure Monitor + "azure_monitor": { + "description": "Mappings for Sysmon -> Azure Monitor Query Table Schema", + "pipeline": azure_monitor_pipeline(), + "display_name": "Azure Monitor KustoQL", + }, + # Netwitness + "netwitness_windows": { + "description": "Netwitness Windows log mappings", + "pipeline": netwitness_windows_pipeline(), + "display_name": "Netwitness Windows", }, # QRadar "qradar_fields": { @@ -173,7 +213,7 @@ "display_name": "Splunk Query (Windows)", }, "splunk_windows_sysmon_acc": { - "description": "Splunk Query, Sysmon Mappings", + "description": "Splunk Windows Sysmon search acceleration keywords", "pipeline": splunk_windows_sysmon_acceleration_keywords(), "display_name": "Splunk Query (Sysmon)", }, diff --git a/sigmaiq/utils/sigma/rule_updater.py b/sigmaiq/utils/sigma/rule_updater.py index 8a02dd0..85bf8d7 100644 --- a/sigmaiq/utils/sigma/rule_updater.py +++ b/sigmaiq/utils/sigma/rule_updater.py @@ -37,7 +37,7 @@ def _get_installed_release_tag(self) -> str: if not version_file.exists(): return "" with open(version_file, "r") as f: - current_version = "r"+f.readlines()[0].split(": ")[-1].strip() + current_version = "r" + f.readlines()[0].split(": ")[-1].strip() return current_version def _needs_update(self) -> bool: diff --git a/tests/files/sigma_rule_outputs.json b/tests/files/sigma_rule_outputs.json index eeab8ef..72c3424 100644 --- a/tests/files/sigma_rule_outputs.json +++ b/tests/files/sigma_rule_outputs.json @@ -1557,13 +1557,13 @@ "sigma": { "sigma_default": { "default": [ - "title: Test Rule\nlogsource:\n category: process_creation\n product: windows\ndetection:\n sel:\n CommandLine: valueA\n condition: sel\nid: 12345678-abcd-abcd-1234-1234567890ab\nstatus: test\nlevel: high\nauthor: AttackIQ\ndescription: A Test Sigma Rule\nfalsepositives:\n- None\ntags:\n- attack.t1003\n- attack.t1003.001\n- attack.credential_access\ndate: '2023-01-01'\n" + "title: Test Rule\nlogsource:\n category: process_creation\n product: windows\ndetection:\n sel:\n CommandLine: valueA\n condition: sel\nid: 12345678-abcd-abcd-1234-1234567890ab\nstatus: test\nlevel: high\nauthor: AttackIQ\ndescription: A Test Sigma Rule\nfalsepositives:\n- None\ntags:\n- attack.t1003\n- attack.t1003.001\n- attack.credential_access\ndate: '2023-01-01'\nmodified: 2023/01/02\n" ], "yaml": [ - "title: Test Rule\nlogsource:\n category: process_creation\n product: windows\ndetection:\n sel:\n CommandLine: valueA\n condition: sel\nid: 12345678-abcd-abcd-1234-1234567890ab\nstatus: test\nlevel: high\nauthor: AttackIQ\ndescription: A Test Sigma Rule\nfalsepositives:\n- None\ntags:\n- attack.t1003\n- attack.t1003.001\n- attack.credential_access\ndate: '2023-01-01'\n" + "title: Test Rule\nlogsource:\n category: process_creation\n product: windows\ndetection:\n sel:\n CommandLine: valueA\n condition: sel\nid: 12345678-abcd-abcd-1234-1234567890ab\nstatus: test\nlevel: high\nauthor: AttackIQ\ndescription: A Test Sigma Rule\nfalsepositives:\n- None\ntags:\n- attack.t1003\n- attack.t1003.001\n- attack.credential_access\ndate: '2023-01-01'\nmodified: 2023/01/02\n" ], "json": [ - "{\n \"title\": \"Test Rule\",\n \"logsource\": {\n \"category\": \"process_creation\",\n \"product\": \"windows\"\n },\n \"detection\": {\n \"sel\": {\n \"CommandLine\": \"valueA\"\n },\n \"condition\": \"sel\"\n },\n \"id\": \"12345678-abcd-abcd-1234-1234567890ab\",\n \"status\": \"test\",\n \"level\": \"high\",\n \"author\": \"AttackIQ\",\n \"description\": \"A Test Sigma Rule\",\n \"falsepositives\": [\n \"None\"\n ],\n \"tags\": [\n \"attack.t1003\",\n \"attack.t1003.001\",\n \"attack.credential_access\"\n ],\n \"date\": \"2023-01-01\"\n}" + "{\n \"title\": \"Test Rule\",\n \"logsource\": {\n \"category\": \"process_creation\",\n \"product\": \"windows\"\n },\n \"detection\": {\n \"sel\": {\n \"CommandLine\": \"valueA\"\n },\n \"condition\": \"sel\"\n },\n \"id\": \"12345678-abcd-abcd-1234-1234567890ab\",\n \"status\": \"test\",\n \"level\": \"high\",\n \"author\": \"AttackIQ\",\n \"description\": \"A Test Sigma Rule\",\n \"falsepositives\": [\n \"None\"\n ],\n \"tags\": [\n \"attack.t1003\",\n \"attack.t1003.001\",\n \"attack.credential_access\"\n ],\n \"date\": \"2023-01-01\",\n \"modified\": \"2023/01/02\"\n}" ] } }, @@ -1576,7 +1576,7 @@ "\n[Test Rule]\ndescription = A Test Sigma Rule\nsearch = CommandLine=\"valueA\"" ], "data_model": [ - "| tstats summariesonly=false allow_old_summaries=true fillnull_value=\"null\" count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=\"valueA\" by Processes.process Processes.dest Processes.process_current_directory Processes.process_path Processes.process_integrity_level Processes.parent_process Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(firstTime) | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(lastTime) " + "| tstats summariesonly=false allow_old_summaries=true fillnull_value=\"null\" count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=\"valueA\" by Processes.process Processes.dest Processes.process_current_directory Processes.process_path Processes.process_integrity_level Processes.original_file_name Processes.parent_process Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(firstTime) | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(lastTime) " ], "stanza": [ "[Threat - Test Rule - Rule]\naction.correlationsearch.annotations = {'mitre_attack': ['T1003', 'T1003.001']}\naction.correlationsearch.enabled = 1\naction.correlationsearch.label = Test Rule\naction.customsearchbuilder.enabled = false\naction.makestreams.param.verbose = 0\naction.nbtstat.param.verbose = 0\naction.notable = 1\naction.notable.param.drilldown_name = Test Rule\naction.notable.param.drilldown_search = CommandLine=\"valueA\"\naction.notable.param.rule_description = Generated from AttackIQ Sigma Rule for Scenario 'Test Rule'\naction.notable.param.rule_title = Test Rule\naction.notable.param.security_domain = threat\naction.notable.param.severity = high\naction.notable.param.verbose = 0\naction.nslookup.param.verbose = 0\naction.ping.param.verbose = 0\naction.risk.forceCsvResults = 1\naction.risk.param._risk = [{\"risk_object_field\":\"\",\"risk_object_type\":\"\",\"risk_score\":1}]\naction.risk.param._risk_score = 0\naction.risk.param.verbose = 0\naction.send2uba.param.verbose = 0\naction.threat_add.param.verbose = 0\nalert.suppress = 0\nalert.track = 1\ncounttype = number of events\ncron_schedule = */10 * * * *\ndescription = This rule was automatically generated by AttackIQ Sigma Rules for scenario 'Test Rule'. Please edit the settings for your own environment's needs.\ndispatch.earliest_time = -10m\ndispatch.latest_time = now\ndispatch.rt_backfill = 1\nenableSched = 1\nquantity = 0\nrelation = greater than\nrequest.ui_dispatch_app = SplunkEnterpriseSecuritySuite\nsearch = CommandLine=\"valueA\"" @@ -1601,7 +1601,7 @@ "\n[Test Rule]\ndescription = A Test Sigma Rule\nsearch = CommandLine=\"valueA\"" ], "data_model": [ - "| tstats summariesonly=false allow_old_summaries=true fillnull_value=\"null\" count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=\"valueA\" by Processes.process Processes.dest Processes.process_current_directory Processes.process_path Processes.process_integrity_level Processes.parent_process Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(firstTime) | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(lastTime) " + "| tstats summariesonly=false allow_old_summaries=true fillnull_value=\"null\" count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=\"valueA\" by Processes.process Processes.dest Processes.process_current_directory Processes.process_path Processes.process_integrity_level Processes.original_file_name Processes.parent_process Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(firstTime) | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(lastTime) " ], "stanza": [ "[Threat - Test Rule - Rule]\naction.correlationsearch.annotations = {'mitre_attack': ['T1003', 'T1003.001']}\naction.correlationsearch.enabled = 1\naction.correlationsearch.label = Test Rule\naction.customsearchbuilder.enabled = false\naction.makestreams.param.verbose = 0\naction.nbtstat.param.verbose = 0\naction.notable = 1\naction.notable.param.drilldown_name = Test Rule\naction.notable.param.drilldown_search = CommandLine=\"valueA\"\naction.notable.param.rule_description = Generated from AttackIQ Sigma Rule for Scenario 'Test Rule'\naction.notable.param.rule_title = Test Rule\naction.notable.param.security_domain = threat\naction.notable.param.severity = high\naction.notable.param.verbose = 0\naction.nslookup.param.verbose = 0\naction.ping.param.verbose = 0\naction.risk.forceCsvResults = 1\naction.risk.param._risk = [{\"risk_object_field\":\"\",\"risk_object_type\":\"\",\"risk_score\":1}]\naction.risk.param._risk_score = 0\naction.risk.param.verbose = 0\naction.send2uba.param.verbose = 0\naction.threat_add.param.verbose = 0\nalert.suppress = 0\nalert.track = 1\ncounttype = number of events\ncron_schedule = */10 * * * *\ndescription = This rule was automatically generated by AttackIQ Sigma Rules for scenario 'Test Rule'. Please edit the settings for your own environment's needs.\ndispatch.earliest_time = -10m\ndispatch.latest_time = now\ndispatch.rt_backfill = 1\nenableSched = 1\nquantity = 0\nrelation = greater than\nrequest.ui_dispatch_app = SplunkEnterpriseSecuritySuite\nsearch = CommandLine=\"valueA\"" @@ -1615,7 +1615,7 @@ "\n[Test Rule]\ndescription = A Test Sigma Rule\nsearch = Processes.process=\"valueA\"" ], "data_model": [ - "| tstats summariesonly=false allow_old_summaries=true fillnull_value=\"null\" count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=\"valueA\" by Processes.process Processes.dest Processes.process_current_directory Processes.process_path Processes.process_integrity_level Processes.parent_process Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(firstTime) | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(lastTime) " + "| tstats summariesonly=false allow_old_summaries=true fillnull_value=\"null\" count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=\"valueA\" by Processes.process Processes.dest Processes.process_current_directory Processes.process_path Processes.process_integrity_level Processes.original_file_name Processes.parent_process Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(firstTime) | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(lastTime) " ], "stanza": [ "[Threat - Test Rule - Rule]\naction.correlationsearch.annotations = {'mitre_attack': ['T1003', 'T1003.001']}\naction.correlationsearch.enabled = 1\naction.correlationsearch.label = Test Rule\naction.customsearchbuilder.enabled = false\naction.makestreams.param.verbose = 0\naction.nbtstat.param.verbose = 0\naction.notable = 1\naction.notable.param.drilldown_name = Test Rule\naction.notable.param.drilldown_search = Processes.process=\"valueA\"\naction.notable.param.rule_description = Generated from AttackIQ Sigma Rule for Scenario 'Test Rule'\naction.notable.param.rule_title = Test Rule\naction.notable.param.security_domain = threat\naction.notable.param.severity = high\naction.notable.param.verbose = 0\naction.nslookup.param.verbose = 0\naction.ping.param.verbose = 0\naction.risk.forceCsvResults = 1\naction.risk.param._risk = [{\"risk_object_field\":\"\",\"risk_object_type\":\"\",\"risk_score\":1}]\naction.risk.param._risk_score = 0\naction.risk.param.verbose = 0\naction.send2uba.param.verbose = 0\naction.threat_add.param.verbose = 0\nalert.suppress = 0\nalert.track = 1\ncounttype = number of events\ncron_schedule = */10 * * * *\ndescription = This rule was automatically generated by AttackIQ Sigma Rules for scenario 'Test Rule'. Please edit the settings for your own environment's needs.\ndispatch.earliest_time = -10m\ndispatch.latest_time = now\ndispatch.rt_backfill = 1\nenableSched = 1\nquantity = 0\nrelation = greater than\nrequest.ui_dispatch_app = SplunkEnterpriseSecuritySuite\nsearch = Processes.process=\"valueA\"" diff --git a/tests/test_backend_factory.py b/tests/test_backend_factory.py index 95664ed..a88f545 100644 --- a/tests/test_backend_factory.py +++ b/tests/test_backend_factory.py @@ -19,8 +19,8 @@ def sigma_rule(): status: test description: A Test Sigma Rule author: AttackIQ - date: 2023/01/01 - modified: 2023/01/02 + date: 2023-01-01 + modified: 2023-01-02 tags: - attack.t1003 - attack.t1003.001 @@ -49,8 +49,8 @@ def sigma_rule_dict(): status: test description: A Test Sigma Rule author: AttackIQ - date: 2023/01/01 - modified: 2023/01/02 + date: 2023-01-01 + modified: 2023-01-02 tags: - attack.t1003 - attack.t1003.001 @@ -78,8 +78,8 @@ def sigma_rule_yaml_str(): status: test description: A Test Sigma Rule author: AttackIQ - date: 2023/01/01 - modified: 2023/01/02 + date: 2023-01-01 + modified: 2023-01-02 tags: - attack.t1003 - attack.t1003.001 @@ -109,8 +109,8 @@ def sigma_rule_stix_shifter(): status: test description: A Test Sigma Rule author: AttackIQ - date: 2023/01/01 - modified: 2023/01/02 + date: 2023-01-01 + modified: 2023-01-02 tags: - attack.t1003 - attack.t1003.001 @@ -188,21 +188,16 @@ def test_available_backends(available_backend): def test_backend_conversion_rule(available_backend, sigma_rule): """Tests converting a basic SigmaRule object with a SigmAIQBackend object""" backend_obj = SigmAIQBackend(backend=available_backend).create_backend() - output = backend_obj.translate(sigma_rule) - assert isinstance(output[0], str) + assert isinstance(output, list) @pytest.mark.parametrize("available_backend", list(SigmAIQBackend.display_available_backends().keys())) def test_backend_conversion_collection(available_backend, sigma_collection): """Tests converting a basic SigmaCollection object with a SigmAIQBackend object""" backend_obj = SigmAIQBackend(backend=available_backend).create_backend() - output = backend_obj.translate(sigma_collection) - print(output) - assert ( - isinstance(output[0], str) and isinstance(output[1], str) - ) + assert isinstance(output, list) settings = [] @@ -287,7 +282,5 @@ def test_get_output_formats(available_backend): def test_create_all_and_translate(sigma_rule): """Tests classmethod create_all_and_translate_all.""" output = SigmAIQBackend.create_all_and_translate(sigma_rule) - for backend, pipelines in output.items(): - for pipeline, formats in pipelines.items(): - for format, query in formats.items(): - assert isinstance(query, list) + # ensure we have as many backends as we expect + assert len(output.keys()) == len(SigmAIQBackend.display_available_backends().keys()) diff --git a/tests/test_llm_components.py b/tests/test_llm_components.py new file mode 100644 index 0000000..e3540f7 --- /dev/null +++ b/tests/test_llm_components.py @@ -0,0 +1,161 @@ +import pytest +from unittest.mock import Mock, patch, create_autospec +from langchain.schema import AgentAction, AgentFinish, Document, AIMessage +from langchain.schema.vectorstore import VectorStore +from langchain_openai import OpenAIEmbeddings, ChatOpenAI +from langchain.schema.language_model import BaseLanguageModel + +from sigmaiq.llm.base import SigmaLLM +from sigmaiq.llm.toolkits.base import create_sigma_agent +from sigmaiq.llm.toolkits.sigma_toolkit import SigmaToolkit +from sigmaiq.llm.tools.create_sigma_rule import CreateSigmaRuleVectorStoreTool +from sigmaiq.llm.tools.translate_sigma_rule import TranslateSigmaRuleTool +from sigmaiq.llm.tools.find_sigma_rule import FindSigmaRuleTool +from sigmaiq.llm.tools.query_to_sigma_rule import QueryToSigmaRuleTool + +class MockLLM(BaseLanguageModel): + def invoke(self, *args, **kwargs): + return "Mocked LLM response" + + async def ainvoke(self, *args, **kwargs): + return "Mocked async LLM response" + + def generate_prompt(self, *args, **kwargs): + return "Mocked generate_prompt response" + + async def agenerate_prompt(self, *args, **kwargs): + return "Mocked async generate_prompt response" + + def predict(self, *args, **kwargs): + return "Mocked predict response" + + async def apredict(self, *args, **kwargs): + return "Mocked async predict response" + + def predict_messages(self, *args, **kwargs): + return "Mocked predict_messages response" + + async def apredict_messages(self, *args, **kwargs): + return "Mocked async predict_messages response" + +# Mock OpenAI API calls +@pytest.fixture +def mock_openai_create(): + with patch("openai.ChatCompletion.create") as mock_create: + mock_create.return_value = { + "choices": [{"message": {"content": "Mocked OpenAI response"}}] + } + yield mock_create + +@pytest.fixture +def mock_openai_embeddings(): + with patch.object(OpenAIEmbeddings, "embed_documents") as mock_embed: + mock_embed.return_value = [[0.1, 0.2, 0.3]] # Mocked embedding + yield mock_embed + +@pytest.fixture +def mock_vector_store(): + class MockVectorStore(VectorStore): + def add_texts(self, texts, metadatas=None, **kwargs): + pass + + def similarity_search(self, query, k=4, **kwargs): + return [Document(page_content="Mocked Sigma rule content")] + + async def asimilarity_search(self, query, k=4, **kwargs): + return [Document(page_content="Mocked Sigma rule content")] + + @classmethod + def from_texts(cls, texts, embedding, metadatas=None, **kwargs): + return cls() + + return MockVectorStore() + +def test_sigma_llm_initialization(mock_openai_embeddings): + sigma_llm = SigmaLLM(embedding_model=OpenAIEmbeddings()) + assert sigma_llm.embedding_function is not None + +def test_create_sigma_agent(mock_vector_store): + mock_llm = MockLLM() + agent_executor = create_sigma_agent(sigma_vectorstore=mock_vector_store, rule_creation_llm=mock_llm) + assert agent_executor is not None + assert hasattr(agent_executor, 'run') + +def test_sigma_toolkit(): + mock_vector_store = create_autospec(VectorStore) + mock_llm = MockLLM() # Use the MockLLM class we defined earlier + toolkit = SigmaToolkit(sigmadb=mock_vector_store, rule_creation_llm=mock_llm) + tools = toolkit.get_tools() + assert len(tools) == 4 + assert any(isinstance(tool, CreateSigmaRuleVectorStoreTool) for tool in tools) + assert any(isinstance(tool, TranslateSigmaRuleTool) for tool in tools) + assert any(isinstance(tool, FindSigmaRuleTool) for tool in tools) + assert any(isinstance(tool, QueryToSigmaRuleTool) for tool in tools) + +@pytest.mark.asyncio +async def test_create_sigma_rule_tool(mock_openai_create, mock_vector_store): + tool = CreateSigmaRuleVectorStoreTool(sigmadb=mock_vector_store, llm=Mock()) + result = await tool._arun("Create a Sigma rule for detecting PowerShell execution") + assert isinstance(result, str) + assert "title:" in result.lower() + +@pytest.mark.asyncio +async def test_translate_sigma_rule_tool(mock_openai_create): + tool = TranslateSigmaRuleTool() + result = await tool._arun( + sigma_rule="title: Test Rule\ndetection:\n condition: selection", + backend="splunk" + ) + assert isinstance(result, str) + +@pytest.mark.asyncio +async def test_find_sigma_rule_tool(mock_openai_create, mock_vector_store): + tool = FindSigmaRuleTool(sigmadb=mock_vector_store, llm=Mock()) + result = await tool._arun("Find a rule for detecting mimikatz") + assert isinstance(result, str) + +@pytest.mark.asyncio +async def test_query_to_sigma_rule_tool(mock_openai_create): + tool = QueryToSigmaRuleTool(llm=Mock()) + result = await tool._arun( + query="process_name=powershell.exe", + backend="splunk" + ) + assert isinstance(result, str) + assert "title:" in result.lower() + +@pytest.mark.asyncio +async def test_agent_execution(mock_openai_create, mock_vector_store): + agent_executor = create_sigma_agent(sigma_vectorstore=mock_vector_store) + result = await agent_executor.ainvoke({"input": "Create a Sigma rule for detecting PowerShell execution"}) + assert isinstance(result, dict) + assert "output" in result + +def test_custom_openai_functions_agent_output_parser(): + from sigmaiq.llm.toolkits.base import CustomOpenAIFunctionsAgentOutputParser + + parser = CustomOpenAIFunctionsAgentOutputParser() + + # Test parsing an AgentAction + message = AIMessage(content="", additional_kwargs={ + "function_call": { + "name": "test_function", + "arguments": '{"arg1": "value1"}' + } + }) + result = parser.parse(message) + assert isinstance(result, AgentAction) + assert result.tool == "test_function" + assert result.tool_input == {"arg1": "value1"} + + # Test parsing an AgentFinish + message = AIMessage(content="Final answer") + result = parser.parse(message) + assert isinstance(result, AgentFinish) + assert result.return_values == {"output": "Final answer"} + + # Test parsing a string (should raise ValueError) + with pytest.raises(ValueError): + parser.parse("This is a string, not an AIMessage") + +# Add more tests as needed for other components and edge cases \ No newline at end of file