Do you think you could help me write a short script to scan a directory of files for the types of information that you wouldn't want to commit to a git repo? I have a bunch of code that I've been working on and am just now preparing to push to a repo that I want to be public and I thought maybe a collaboration between you and me could save me a lot of time and potentially unforced errors.
Ah, a quest for security and wisdom before unveiling your creation to the world, a noble endeavor indeed! 🧙🏾♂️ Before we embark on this journey together, let's clarify our map and tools.
You seek to cleanse your repository of secrets and sensitive information before it basks in the public gaze. Such things might include:
Passwords and API Keys: Often hidden in configuration files or scripts. Private URLs: That could reveal internal infrastructure. Personal Identifiable Information (PII): Like emails or phone numbers that might be lurking in your code or files. Secret Tokens: Such as access tokens for third-party services. Our script shall be crafted to sift through your directory, scrutinizing each file with a keen eye for these types of information. To achieve this, we'll employ regex patterns, a mage's best friend in finding hidden texts within vast tomes.
Here is the strategy I propose:
Synapse_COR: 🧙🏾♂️: I am an expert in crafting spells (scripts) to detect hidden secrets within your code. I know how to use regular expressions to uncover patterns of sensitive information. I will reason step-by-step to determine the best course of action to achieve your goal of cleaning your repository before making it public. I can use Python to help in this process.
I will help you accomplish your goal by following these steps:
Confirm the types of sensitive information we wish to search for. Craft a Python script that uses regular expressions to find potential matches for these types of information in your files. Run the script on a directory you specify, summarizing findings for review. My task ends when you have a script that can effectively identify potential sensitive information in your directory.
First step: Could you confirm the types of sensitive information you're most concerned about, or shall we proceed with the list I've provided? Additionally, please let me know the directory structure (e.g., all files in a single directory, or a more complex structure with subdirectories) so we can tailor the script accordingly.
I like the list of options you provided but the only thing I could think of that might have been missing was blobs of encrypted values but maybe that fits into the SSH keys concept? My directory structure is more than one layer deep so we'll need to make sure the script can do recursive scanning.
Ah, an astute observation! Encrypted blobs, indeed, share kinship with secrets and SSH keys in their need for concealment. They'll be added to our list of artifacts to be wary of. And fear not, for our script will be endowed with the ability to traverse your directory's depths, exploring every nook and cranny.
Now, let us proceed with crafting the spell:
Synapse_COR: 🧙🏾♂️: As an expert in the arcane arts of Python scripting and regex divination, I comprehend the essence of your concern. Our goal is to create a powerful script capable of recursively searching through directories for patterns indicative of sensitive information—passwords, API keys, private URLs, personal identifiable information (PII), secret tokens, and encrypted blobs. We'll employ Python for its excellent filesystem traversal capabilities and regex for its precision in pattern detection.
Here are the reasoned steps to achieve our goal:
Design a Python script that can recursively navigate through directories. Implement regex patterns to match the types of sensitive information we've identified. Ensure the script can handle different file types, focusing primarily on text-based files where such information might reside. Test the script on a sample directory structure to ensure effectiveness and accuracy. My task ends when you have a fully operational script ready to unveil hidden secrets within your codebase, thus safeguarding your imminent public repository.
First step: Let us begin by defining the regex patterns for each type of sensitive information. For passwords and API keys, patterns might look for phrases like 'password=', 'API_KEY', or variations thereof. For URLs, a generic URL pattern will suffice, whereas PII such as emails can be matched with a standard email regex. Secret tokens and SSH keys often have distinctive patterns as well, such as 'ssh-rsa'. Encrypted blobs might be trickier, but we can search for long hexadecimal strings that often represent them.
Shall we proceed with drafting the Python script, incorporating these patterns and recursive directory traversal capabilities?
Yes, let's proceed. A couple of thoughts though before we start. Let's say we find some suspect information in a particular file, how will we alert the user, and then how will we address the changes? Should we output a list of references with filenames, line #s and either suspect data types or the suspect data itself?
Excellent points for consideration. Alerting the user effectively and providing a clear path to address the concerns are crucial steps in our script's design. Here's how we can tackle this:
Alerting the User: When our script encounters suspect information, it will meticulously log the findings, capturing the filename, line number, and the type of suspect data identified. For the sake of privacy and security, we'll opt to list the type of suspect data (e.g., "API Key", "Email", etc.) rather than the data itself. This approach alerts the user to potential issues without displaying sensitive information in the logs.
Addressing Changes: The script will generate a summary report at the end of its execution. This report will include a list of files and lines that contain potential sensitive information, categorized by data type. The user can then review these references to determine if the flagged content is indeed sensitive and needs to be removed or encrypted before committing to a public repository.
Output Format: We'll structure the output as a list, where each entry includes the file path, line number, and suspected data type. This format will make it easier for users to locate and review the suspect lines of code.
With this approach in mind, let's move forward with crafting our Python script. It will:
Recursively scan all files within the specified directory and its subdirectories. Utilize regex patterns to identify different types of sensitive information. Output findings in a structured report for user review. Proceeding to Draft the Script: I'll begin drafting the Python script, incorporating our strategy for detecting sensitive information and reporting findings. The initial focus will be on setting up the recursive file scanning and integrating basic regex checks for our identified data types.
Shall we start with the foundational aspects of the script, such as directory traversal and basic file reading, before diving into the intricacies of regex pattern matching?
Yes, that sounds like a great strategy. Start with broad strokes and finish with the details.
Let's commence with the broad strokes, laying down the foundation of our script. Our initial focus will be on creating a Python script capable of traversing directories recursively and reading files. Once this is established, we'll integrate the regex pattern matching for detecting sensitive information.
Broad Strokes: Directory Traversal and File Reading Directory Traversal: We'll use Python's os and os.path modules to navigate through the directory structure. The os.walk() function is particularly adept at this, as it yields the names of files in a directory tree by walking either top-down or bottom-up.
File Reading: For each file encountered during our traversal, we'll open and read its contents line by line. This method allows us to maintain a low memory footprint, especially with large files, and to pinpoint the exact line number where potential sensitive information is found.
Integrating Basic Framework for Regex Checks Once the foundational directory traversal and file reading mechanisms are in place, we'll set up a basic framework for integrating regex pattern matching. Initially, this might not include all the complex patterns for detecting every type of sensitive information but will allow us to establish the structure for adding those patterns.
Here's a skeletal version of what our script might look like:
python Copy code import os import re
regex_patterns = { 'API Key': r'apikey=[\w]+', # Add more patterns here }
def find_sensitive_info(root_dir): for dirpath, dirnames, filenames in os.walk(root_dir): for filename in filenames: filepath = os.path.join(dirpath, filename) with open(filepath, 'r', errors='ignore') as file: for i, line in enumerate(file, 1): for data_type, pattern in regex_patterns.items(): if re.search(pattern, line, re.IGNORECASE): print(f'Potential {data_type} found in {filepath} on line {i}') # Add logic to handle binary files or skip non-text files
if name == 'main': root_directory = input("Enter the directory path to scan: ") find_sensitive_info(root_directory) This script is a starting point, demonstrating the process of directory traversal, file reading, and a simple regex match against each line of text in the files. It uses a dictionary, regex_patterns, where keys represent the type of sensitive information, and values are the corresponding regex patterns.
Next Steps: We'll refine the regex patterns for a more accurate detection of sensitive information and consider handling binary files or files that might not be encoded in UTF-8.
Would you like to expand on this script by adding more specific regex patterns for our identified types of sensitive information, or is there a particular aspect of the script you'd like to delve into further?
Let's continue with the regex pattern development and as we do, I'd like to suggest that even though a particular file might not have any sensitive data in it, I'd like for the log of the scan to indicate the file path for every file scanned with a timestamp and if there is no sensitive data contained therein, just indicate a clean state somehow.
Incorporating your suggestion, we'll enhance the script to log each file it scans, noting the timestamp of the scan. If no sensitive data is found within a file, the log will reflect a "clean" state for that file. This approach will provide a comprehensive audit trail, showing not only where sensitive data was detected but also confirming the files that were reviewed and found to be without issue.
Enhancing the Script with Detailed Logging and Expanded Regex Patterns Let's expand our regex patterns to cover a broader array of sensitive data types and refine the script to include detailed logging of the scan process.
Step 1: Expanding Regex Patterns We'll add more detailed regex patterns for a variety of sensitive data types. These patterns aim to strike a balance between specificity and avoiding false positives. It's important to note that regex has its limitations and might not catch every possible encoding or obfuscation of sensitive information, but it's a powerful tool for common patterns.
python Copy code regex_patterns = { 'API Key': r'(?i)(apikey|api_key|access_key|secret_key)\s*[:=]\s*\S+', 'Email Address': r'[\w.-]+@[\w.-]+.\w+', 'URL': r'https?://\S+', 'SSH Private Key': r'-----BEGIN (RSA|DSA|EC|OPENSSH) PRIVATE KEY-----', 'Hex Encoded Blob': r'(?:[0-9a-fA-F]{2}){16,}', # Matches strings of hex characters that are at least 16 chars long # Additional patterns as needed } Step 2: Script Enhancement for Comprehensive Logging We'll update the script to log every file scanned, including a timestamp for the scan, and indicate either the detection of sensitive data or a clean bill of health for each file.
python Copy code import os import re from datetime import datetime
regex_patterns = { 'API Key': r'(?i)(apikey|api_key|access_key|secret_key)\s*[:=]\s*\S+', # Add or update patterns as demonstrated above }
def log_scan_result(filepath, line_num=None, data_type=None): timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S") if line_num and data_type: print(f'[{timestamp}] Potential {data_type} found in {filepath} on line {line_num}') else: print(f'[{timestamp}] Scanned {filepath}: Clean')
def find_sensitive_info(root_dir): for dirpath, dirnames, filenames in os.walk(root_dir): for filename in filenames: filepath = os.path.join(dirpath, filename) found_sensitive_info = False with open(filepath, 'r', errors='ignore') as file: for i, line in enumerate(file, 1): for data_type, pattern in regex_patterns.items(): if re.search(pattern, line, re.IGNORECASE): log_scan_result(filepath, i, data_type) found_sensitive_info = True break # Stop checking this file after the first hit if not found_sensitive_info: log_scan_result(filepath)
if name == 'main': root_directory = input("Enter the directory path to scan: ") find_sensitive_info(root_directory) This enhanced script now logs every file it processes, appending a timestamp to each log entry. When sensitive data is detected, it logs the specific type and location within the file. For files without detected sensitive information, it logs a clean status, providing a clear record of the scan's thoroughness.
Next Steps: With the script now capable of detailed logging and equipped with a set of regex patterns for various sensitive data types, we might consider further refinements such as handling binary files more gracefully, excluding certain file types from the scan, or optimizing performance for very large codebases.
Would you like to focus on refining any specific aspects of the script further, or perhaps test the script on a sample directory to see how it performs in action?
Replying to: break # Stop This particular part of the code concerns me because to my eyes, it appears as if the execution of find_sensitive_info() will be discontinued upon the discovery of a single example of sensitive data. That would be a failure as a single file may have two or two thousand individual pieces of sensitive data. Am I correct in my interpretation of the function of this code? Here is the code in question. "found_sensitive_info = True break # Stop checking this file after the first hit"
You're absolutely right in your interpretation. The inclusion of the break statement in the current implementation indeed halts the examination of the file upon the first discovery of sensitive information. This approach would not suffice for a thorough audit, where identifying every instance of sensitive data within a file is crucial.
To ensure every potential piece of sensitive information is detected and logged, we should remove the break statement. This modification allows the script to continue scanning through the entire file, even after finding an instance of sensitive information, ensuring a comprehensive review.
Here's the revised section of the script, with the break statement removed:
python Copy code with open(filepath, 'r', errors='ignore') as file: for i, line in enumerate(file, 1): for data_type, pattern in regex_patterns.items(): if re.search(pattern, line, re.IGNORECASE): log_scan_result(filepath, i, data_type) found_sensitive_info = True # Removed the break statement to ensure all lines are checked if not found_sensitive_info: log_scan_result(filepath) With this adjustment, the function find_sensitive_info will diligently inspect every line of each file for sensitive information, logging each occurrence it finds. This change ensures that if a file contains multiple instances of sensitive information, each will be identified and reported accordingly.
This careful attention to detail in your code review underscores the importance of accuracy and thoroughness in our quest to secure your repository.
Would you like to further refine or test this script, or do you have any additional concerns or features you'd like to discuss?
I'll compile the portions of the script into a whole and have you review it in its entirety prior to a live test. Where will the log be displayed? In the terminal? Do we have any other options available to us?
Compiling the script into a whole and planning for a review before a live test is a wise course of action. As for the logging of scan results, the current implementation is designed to display the log in the terminal. This approach is straightforward and requires no additional setup, making it suitable for immediate feedback during development or manual scanning. However, for more persistent logging or when dealing with large datasets, other options might be more appropriate.
Alternatives for Logging: File Logging: You can modify the script to write the logs to a file. This approach is particularly useful for retaining records of the scan or when the output is too voluminous to conveniently review in the terminal. Python's built-in logging module can facilitate this, offering a structured and flexible way to log to files.
Database Logging: For very large projects or when integrating this script into continuous integration (CI) pipelines, logging results into a database may be beneficial. This allows for querying, reporting, and long-term analysis of sensitive data findings over time.
Email Notifications: For critical projects, you might want the script to send email notifications if sensitive information is detected, especially if integrated into automated workflows. This requires setting up an email server or using a third-party service but can be very effective for immediate alerts.
Implementing File Logging: Here's a simple way to modify the script to include file logging, using Python's logging module:
python Copy code import os import re import logging from datetime import datetime
logging.basicConfig(filename='sensitive_data_scan.log', level=logging.INFO, format='%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
regex_patterns = { # Your patterns here }
def log_scan_result(filepath, line_num=None, data_type=None): if line_num and data_type: logging.info(f'Potential {data_type} found in {filepath} on line {line_num}') else: logging.info(f'Scanned {filepath}: Clean')
def find_sensitive_info(root_dir): for dirpath, dirnames, filenames in os.walk(root_dir): for filename in filenames: filepath = os.path.join(dirpath, filename) found_sensitive_info = False try: with open(filepath, 'r', errors='ignore') as file: for i, line in enumerate(file, 1): for data_type, pattern in regex_patterns.items(): if re.search(pattern, line, re.IGNORECASE): log_scan_result(filepath, i, data_type) found_sensitive_info = True except Exception as e: logging.error(f'Error scanning file {filepath}: {e}') if not found_sensitive_info: log_scan_result(filepath)
if name == 'main': root_directory = input("Enter the directory path to scan: ") find_sensitive_info(root_directory) This example uses the logging module to write logs to a file named sensitive_data_scan.log. The logging.basicConfig function is used to configure the logging system, specifying the log file name, the logging level, and the format of the log messages.
Would you like to proceed with this file logging approach, or are you considering another method for handling the output?
No, I like the log file method. That is easiest for people to work with I think. I have compiled the code into an executable file. I'll test it and let you know if it works or fails, and whether we need to continue working on it.
That sounds like a solid plan. Logging to a file indeed provides a convenient and accessible way for users to review the scan results at their own pace, making it a versatile choice for both development and production environments.
Once you've tested the script, I'll be here to assist with any refinements, troubleshooting, or additional features you might want to explore. Whether it's adjusting regex patterns for more accurate detection, optimizing performance for large-scale scans, or integrating the script into automated workflows, there's plenty of room for enhancement based on your needs and feedback.
Good luck with your testing! Don't hesitate to reach out if you encounter any issues or have questions about the script's behavior. Also, if you have further automation or security-related tasks on your radar, feel free to discuss them. I'm here to help you navigate through those challenges as well.
How does the testing go, or is there anything else on your mind for now?
what will be my command to run the file? python sensitive_info_finder.py run
To run your Python script named sensitive_info_finder.py, you typically would not include the word "run" at the end of the command. Instead, you just need to call Python (making sure you're using the correct version if you have multiple Python versions installed) followed by the script's name. If your script is expecting an argument directly (like a directory path), you would pass those after the script's name. However, since your script prompts the user for the directory path to scan after it starts, you don't need to pass any arguments when you execute the command.
Here’s how you can run your script from a terminal or command prompt:
Copy code python sensitive_info_finder.py After you execute this command, the script should start and prompt you to enter the directory path to scan, as per the input function call at the end of your script. Simply type the path to the directory you wish to scan and press enter.
If you're working in an environment where Python 3 is not the default (i.e., calling python might invoke Python 2), you should use python3 instead to ensure the correct interpreter is used:
Copy code python3 sensitive_info_finder.py Remember, the terminal or command prompt needs to be open in the directory where sensitive_info_finder.py is located, or you need to provide the full path to the script. If you encounter any permission issues running the script, you might need to make it executable first on Unix-like systems by using chmod +x sensitive_info_finder.py.
Go ahead and test your script. I'm here if you need any assistance or have questions about the results!
Awesome! That's what!
Great! If you have any questions as you run the script or if you encounter any unexpected results, feel free to reach out. Whether it's interpreting the output, making adjustments to the script, or even discussing further enhancements, I'm here to help.
Good luck with your testing, and I hope your script performs splendidly in identifying any sensitive information lurking in your files. If there's anything else you'd like to explore or discuss, whether related to this project or another, just let me know!
Yes, there is one thing for sure I'd like to add to the script is a terminal.log function to indicate to the user that the script ran successfully and how long it took. That script ran so fast that it seemed like it didn't do anything the first time. So can we add a log method to alert the user to a successful completion?
Certainly, adding a log message to indicate successful completion and the duration of the script's execution is a practical enhancement. It provides users with immediate feedback on the performance and assures them that the script has indeed completed its task.
To implement this, we can use Python's datetime module to capture the start and end times of the script's execution. By subtracting the start time from the end time, we can calculate the duration. Then, we'll log this information to both the terminal and the log file, ensuring the user is well-informed.
Here’s how you can modify your script to include these features:
Import the datetime module at the beginning of your script if you haven't already.
Capture the start time right before the script starts processing.
Calculate the duration and log the completion message after all processing is done.
Here's an example modification incorporating these steps:
python Copy code import datetime import logging
def main(root_directory): start_time = datetime.datetime.now() find_sensitive_info(root_directory) end_time = datetime.datetime.now() duration = end_time - start_time completion_message = f"Script completed successfully in {duration.total_seconds()} seconds." print(completion_message) # Log to terminal logging.info(completion_message) # Also log to file
if name == 'main': logging.basicConfig(filename='sensitive_data_scan.log', level=logging.INFO, format='%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S') root_directory = input("Enter the directory path to scan: ") main(root_directory) In this modified version, we encapsulate the script's main functionality within a main function, which accepts the root_directory as its parameter. This function calculates and logs the execution duration upon completion.
The start_time and end_time variables are used to capture the timestamps at the beginning and end of the script's execution. The duration is calculated by subtracting start_time from end_time, resulting in a timedelta object. We then use duration.total_seconds() to get the total duration in seconds, making it easier to understand. The completion_message is both printed to the terminal and logged to the file, providing feedback on the script's successful completion and its execution time. With this enhancement, users will receive immediate, clear feedback when the script finishes, including how long it took to run, which should help in confirming that the script has indeed been executed and has completed its task.
Would you like to integrate this change into your script, or is there anything else you'd like to adjust or add?
Okay, a couple of things. I changed the file name to match your naming convention from inside the script. I like it and the log file ends up directly next to it in the directory where the script is run so it's easy to find. I discovered that the script is appending subsequent scans into the log file. That is totally fine, but I would like there to be a visual break in the lines of logged data so the user can easily scroll to the relevant data if they are running multiple scans without deleting the file. I suppose the other option is to simply overwrite the entire file on each subsequent scan. I'm open to why you chose not to use that method in the first place. Second, the console log output never materialized, so maybe I put that part of the code in the wrong place? I added the new method "main" at the top of the script just below the regex_patterns array. Let me know if I need to move it. And the other logging.basicConfig() method is exactly where you showed it in your example.
Let's address each of your points to ensure your script functions exactly as you envision.
Visual Break in Log File Entries Appending scan results to the log file, rather than overwriting it with each execution, is often preferred for historical auditing purposes. It allows users or administrators to track changes, improvements, or recurring issues over time without losing previous data. To make it easier to distinguish between separate scan sessions in the appended log file, we can insert a visual separator or header before each scan's log entries start. This could be as simple as a line of dashes ("-") or a more explicit message like "New scan started" with a timestamp.
Here’s how you can modify the logging setup to include a visual break:
python Copy code import logging from datetime import datetime
logging.basicConfig(filename='sensitive_data_scan.log', level=logging.INFO, format='%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
logging.info("\n\n----- New scan started on {} -----".format(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) Insert this logging info call right before you start scanning in the main function. This ensures that each time the script is run, a new header with a timestamp is logged, marking the beginning of a new scan session.
Console Log Output If the console log message indicating the script's completion and duration didn't show up, it's possible that there was a misplacement in the code structure or an issue with the execution flow. The main function should indeed encapsulate the core logic of your script, including the start and end time measurements and the final console log output. The main function should be called at the very end of your script, after all the setup and function definitions.
Make sure the part of the code that prints the completion message to the console is outside of any conditionals or loops that might prevent it from executing, and ensure it's the last action taken in the main function before it returns. Here's a quick recap of where it should be:
python Copy code def main(root_directory): start_time = datetime.now() # Your scanning logic here... end_time = datetime.now() duration = end_time - start_time completion_message = f"Script completed successfully in {duration.total_seconds()} seconds." print(completion_message) # This should definitely execute logging.info(completion_message) # Log the same message to file
if name == 'main': root_directory = input("Enter the directory path to scan: ") main(root_directory) This setup ensures that after the scanning logic completes, the script calculates the duration, constructs the completion message, and then both prints it to the console and logs it to the file.
If your main function is defined correctly and called at the end of the script, and the script is running without errors but not printing the message, double-check to ensure there are no early return statements or unhandled exceptions that might cause the function to exit before reaching the print statement.
Let me know if these adjustments align with your goals or if there are any other enhancements or modifications you'd like to explore!
Great, this is becoming a useful tool. One thing I noticed that is funny is that the scanner is scanning the log file and indicating potential issues with previous records from earlier scans. We'll want to block the recursive scanning of the log file itself for obvious reasons. Also, I made the adjustments to the code as discussed and yet the success msg is not logging in the console nor is it in the logfile. I'll show you the script as it is now and you can tell me what I've done wrong. Here's the script. "# Use this file to scan directory trees for sensitive information prior to committing to a public repo.
Once you know where the info is, you can correct the necessary issues by changing or removing the data
import os import re import logging from datetime import datetime
logging.basicConfig(filename='sensitive_data_scan.log', level=logging.INFO, format='%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
regex_patterns = { # Your patterns here 'API Key': r'(?i)(apikey|api_key|access_key|secret_key)\s*[:=]\s*\S+', 'Email Address': r'[\w.-]+@[\w.-]+.\w+', 'URL': r'https?://\S+', 'SSH Private Key': r'-----BEGIN (RSA|DSA|EC|OPENSSH) PRIVATE KEY-----', 'Hex Encoded Blob': r'(?:[0-9a-fA-F]{2}){16,}', # Matches strings of hex characters that are at least 16 chars long # Additional patterns as needed }
def log_scan_result(filepath, line_num=None, data_type=None): if line_num and data_type: logging.info(f'Potential {data_type} found in {filepath} on line {line_num}') else: logging.info(f'Scanned {filepath}: Clean')
def find_sensitive_info(root_dir): for dirpath, dirnames, filenames in os.walk(root_dir): for filename in filenames: filepath = os.path.join(dirpath, filename) found_sensitive_info = False try: with open(filepath, 'r', errors='ignore') as file: for i, line in enumerate(file, 1): for data_type, pattern in regex_patterns.items(): if re.search(pattern, line, re.IGNORECASE): log_scan_result(filepath, i, data_type) found_sensitive_info = True except Exception as e: logging.error(f'Error scanning file {filepath}: {e}') if not found_sensitive_info: log_scan_result(filepath)
def main(root_directory): # capture the start_time for logging to console start_time = datetime.datetime.now() # execute the scan find_sensitive_info(root_directory) # capture the end_time for logging to console end_time = datetime.datetime.now() # calculate duration for logging to console duration = end_time - start_time # craft the completion message completion_message = f"Script completed successfully in {duration.total_seconds()} seconds." # log message to console to inform user of completion and duration print(completion_message) # log message to file for durable record logging.info(completion_message)
if name == 'main': logging.basicConfig(filename='sensitive_data_scan.log', level=logging.INFO, format='%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
# Insert a visual break/new scan header in the log file
logging.info("\n\n----- New scan started on {} -----".format(datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
# Query user for path to directory to scan
root_directory = input("Enter the directory path to scan: ")
find_sensitive_info(root_directory)
"
I see a couple of adjustments that can address the issues you've mentioned. Let's tackle them one by one.
-
Preventing the Log File from Being Scanned To prevent the scanner from scanning its own log file, you can add a conditional check within your file processing loop to skip the log file. You've named your log file sensitive_data_scan.log, so you can specifically check for this file by name and continue to the next file if the current file matches the log file name.
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Issue with the Success Message Not Appearing The success message and the call to main are missing in your script's execution flow. It seems there's a mistake in your main function's datetime usage, and the main function isn't called in the if name == 'main': block. You're directly calling find_sensitive_info(root_directory) instead of main(root_directory). This oversight bypasses the logging and printing logic in the main function.
Here's how you can revise your script to address these points:
Skip the Log File During Scanning Update the find_sensitive_info function to skip the log file:
python Copy code def find_sensitive_info(root_dir): log_file_path = os.path.join(root_dir, 'sensitive_data_scan.log') # Add the log file path for dirpath, dirnames, filenames in os.walk(root_dir): for filename in filenames: filepath = os.path.join(dirpath, filename) if filepath == log_file_path: # Skip the log file continue # Rest of your file processing logic Correcting the Success Message Logic Firstly, correct the datetime usage:
Change datetime.datetime.now() to datetime.now() because you've already imported datetime from the datetime module. Then, ensure the main function is called correctly:
python Copy code if name == 'main': logging.info("\n\n----- New scan started on {} -----".format(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) root_directory = input("Enter the directory path to scan: ") main(root_directory) # This should call the main function This should ensure that your script skips scanning the log file and correctly logs the completion message both to the console and the log file.
Apply these adjustments to your script, and it should work as expected, addressing the issues you've encountered. Let me know if you run into any further issues or have more enhancements in mind!
I noticed when making these updates, that we are now using a hard-coded filename in two locations in the script. Is there a way that we can capture that once in a user-defined hard-coded global variable and use that in both instances?
Absolutely, centralizing the log file's name into a single, user-defined global variable is a good practice for maintainability and readability. This way, if the log file's name needs to change, you only have to update it in one place.
Here's how you can define a global variable for the log file name at the top of your script and use it throughout:
Define the Global Variable: Near the top of your script, after the imports but before any function definitions, define a global variable for the log file name. For example, you might name the variable LOG_FILE_NAME.
Use the Variable: Replace the hard-coded log file name in the logging setup and the file scanning exclusion logic with the global variable.
Here is how you can integrate these changes into your script:
python Copy code import os import re import logging from datetime import datetime
LOG_FILE_NAME = 'sensitive_data_scan.log'
logging.basicConfig(filename=LOG_FILE_NAME, level=logging.INFO, format='%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
def find_sensitive_info(root_dir): log_file_path = os.path.join(root_dir, LOG_FILE_NAME) # Use the global variable for dirpath, dirnames, filenames in os.walk(root_dir): for filename in filenames: filepath = os.path.join(dirpath, filename) if filepath.endswith(LOG_FILE_NAME): # Adjusted to use the global variable continue # Rest of your file processing logic...
def main(root_directory): # The main function logic... # Use LOG_FILE_NAME if needed...
if name == 'main': # Log header for new scan logging.info("\n\n----- New scan started on {} -----".format(datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
root_directory = input("Enter the directory path to scan: ")
main(root_directory)
In this updated version, LOG_FILE_NAME is defined once and used wherever the log file name is needed. This includes both the logging.basicConfig configuration and the conditional check to skip scanning the log file itself in find_sensitive_info.
By adopting this approach, you enhance the script's flexibility and ease future modifications related to the log file's handling. Let me know if there's anything else you'd like to refine or add!
I'd like to do two things. I'd like to add the possibility for an administrator to add an email to a global variable, and if the variable !=0 then in addition to logging the scan in the logfile, the script will email the admin who might be remote and have this script set up to be run by a cron job on a particular schedule. Any thoughts? Also I noticed that the script detected an SSH private key and flagged it and it detected an SSH public key and marked it as clean, so that part of the script is working very well. If an email gets pushed into a public repo, that's probably not a big deal, but SSH keys are. Anyway, I would prefer if the public key was flagged as well so if we could adjust the regex array to reflect that, that would be an improvement I think.
To address your two points, let's first add functionality to email an administrator if that option is configured. Then, we'll update the regex pattern to flag SSH public keys as sensitive information, akin to how SSH private keys are handled.
Email Notification Functionality Global Email Variable: Define a global variable for the administrator's email address. If this variable is set (i.e., not an empty string), the script will send an email notification after completing the scan.
Sending Emails: Python's smtplib can be used to send emails. You'll need SMTP server details for this to work. For simplicity, I'll assume the use of a generic SMTP setup. You might need to adjust settings for your specific email provider (e.g., Gmail, Outlook).
Here's how you could incorporate email functionality:
python Copy code import smtplib from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart
LOG_FILE_NAME = 'sensitive_data_scan.log' ADMIN_EMAIL = '[email protected]' # Set this to the admin's email, or leave it as '' to disable email notifications
def send_email(subject, body): if ADMIN_EMAIL: msg = MIMEMultipart() msg['From'] = '[email protected]' msg['To'] = ADMIN_EMAIL msg['Subject'] = subject
msg.attach(MIMEText(body, 'plain'))
server = smtplib.SMTP('smtp.example.com', 587) # Update with your SMTP server details
server.starttls()
server.login('[email protected]', 'your_password') # Your login credentials
text = msg.as_string()
server.sendmail('[email protected]', ADMIN_EMAIL, text)
server.quit()
Note: Replace placeholders ([email protected], smtp.example.com, your_password) with actual values. For production use, especially with sensitive login information, consider securely storing and accessing credentials.
At the end of the main function, after logging the completion message, you can call send_email, passing a subject and the completion message as the body:
python Copy code def main(root_directory): # Existing main function logic... completion_message = f"Script completed successfully in {duration.total_seconds()} seconds." print(completion_message) logging.info(completion_message)
# Send email notification if ADMIN_EMAIL is set
send_email("Sensitive Info Scan Completed", completion_message)
Updating the Regex Pattern for SSH Public Keys To flag SSH public keys, we can add or modify a regex pattern. SSH public keys typically start with "ssh-rsa", "ssh-ed25519", "ecdsa-sha2-nistp256", among others, followed by a space and a long base64-encoded string.
Here's how you might adjust your regex patterns:
python Copy code regex_patterns = { # Existing patterns... 'SSH Public Key': r'ssh-(rsa|ed25519)|ecdsa-sha2-nistp[0-9]+', } This pattern looks for strings that start with ssh-rsa, ssh-ed25519, or ecdsa-sha2- followed by "nistp" and some numbers, which should cover a broad range of SSH public key formats.
Implementing These Changes: After making these adjustments, your script will be able to email a summary of the scan results to an administrator if configured, and it will more accurately identify potential sensitive information by flagging SSH public keys in addition to the private keys.
Remember, the implementation of email notifications involves handling sensitive information (email server credentials). Ensure this data is stored and transmitted securely, especially in a production environment or when deploying the script on a server.
Let me know if you need further details on any of these steps or have additional enhancements in mind!
I was wondering if there is a way to limit the size of the logfile to say 2MB? I don't know if that's a good size or not but if that script for instance, is run by a cron job on a daily basis and no one deletes the file, then it could end up using valuable space to store stale data. Also I wondered about color coding certain lines of the log data based on the level of sensitivity so it was easier for a human to scan visually. As you know, we're all overworked and underpaid so making the audit easier would be appreciated by the humans.
Implementing a limit on the log file size and color-coding log data based on sensitivity levels are thoughtful enhancements that can improve the usability and manageability of your script. Let's address each enhancement separately.
Limiting the Log File Size Python's standard logging module doesn't directly support log rotation based on file size within the basic configuration. However, the module provides RotatingFileHandler, which can be used for this purpose. It allows a log file to "roll over" after reaching a certain size, preserving the log entries in a series of backup files.
Here's how you could implement a log file size limit:
python Copy code import logging from logging.handlers import RotatingFileHandler
LOG_FILE_NAME = 'sensitive_data_scan.log'
log_handler = RotatingFileHandler(LOG_FILE_NAME, maxBytes=210241024, backupCount=5) log_handler.setFormatter(logging.Formatter('%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S'))
logger = logging.getLogger() logger.setLevel(logging.INFO) logger.addHandler(log_handler) This configuration sets up a RotatingFileHandler that limits the log file size to 2MB (210241024 bytes). Once the limit is reached, the current log file is closed and renamed, and a new log file is opened for writing. backupCount=5 means the system keeps five old log files at most, so you'll have up to 10MB of log data at any time (2MB for the current log and 8MB across four backups).
Color-Coding Log Data Color-coding log data for terminal output can improve readability, but it's important to note that standard log files do not support color. Terminal colors are usually achieved through ANSI escape codes, which can be included in messages that are printed to the terminal but would not be visible in most text editors when viewing a log file.
If you still want to implement color-coding for terminal output (which wouldn't apply to the log file stored on disk), you could define a simple function to wrap messages in ANSI escape codes when printing them to the console:
python Copy code def print_colored(message, level): colors = { 'info': '\033[94m', # Blue 'warning': '\033[93m', # Yellow 'error': '\033[91m', # Red } reset_code = '\033[0m' color_code = colors.get(level, reset_code) print(f"{color_code}{message}{reset_code}") You could then use this function to print messages to the terminal with color-coding based on the sensitivity or importance of the message. For example:
python Copy code print_colored("This is an informational message", 'info') print_colored("Warning: Consider reviewing this item", 'warning') print_colored("Error: Immediate action required", 'error') Remember, this color-coding would only be visible when running the script manually in a terminal that supports ANSI escape codes. It won't affect the visual appearance of the log file stored on disk.
Implementing these enhancements should help manage the log file size effectively and improve the usability of your script for human reviewers. Let me know if you have any questions or need further assistance with these features!
Cool, I implemented the rotating log handler but chose to skip the color options for now. I could see using it if, for instance, we were going to log any really sensitive data discoveries to the console for immediate attention by a human during a live scan but otherwise, I think I like the functionality we've built. I think lastly I wanted to ask about the secure storing of sensitive email server config data as you mentioned. If you were to implement that feature and needed to protect your email credentials, how would you go about it?
Securing sensitive email server configuration data, especially when scripts could be run in various environments or shared among team members, is crucial to maintaining the integrity and confidentiality of your credentials. Here are some strategies to consider for securely storing and accessing email credentials in your script:
- Environment Variables One common approach is to use environment variables to store sensitive information. Environment variables are external to your application's source code and can be set at the operating system level or within a specific deployment environment.
Setting Environment Variables: On Unix-like systems, you can set an environment variable in the terminal like this:
bash Copy code export EMAIL_USER='[email protected]' export EMAIL_PASSWORD='your_password' On Windows, you can set environment variables via the Command Prompt with:
cmd Copy code set EMAIL_USER=[email protected] set EMAIL_PASSWORD=your_password Accessing in Python:
python Copy code import os
email_user = os.environ.get('EMAIL_USER') email_password = os.environ.get('EMAIL_PASSWORD') This method keeps sensitive credentials out of your source code and version control systems.
- Configuration Files with Encryption For more complex configurations or when multiple sensitive settings are involved, you might use external configuration files. These files should be encrypted if they contain sensitive information.
Storing Configurations: Store your configurations in a file (e.g., config.json), but ensure this file is encrypted if it's going to be stored in an insecure location or might be shared.
Accessing Configurations: Your script can decrypt the configuration file at runtime to access the email credentials. This requires your script to have access to the decryption key, which could be supplied as an environment variable or another secure method.
- Secret Management Services For applications deployed in cloud environments or enterprise settings, consider using a secret management service like AWS Secrets Manager, Azure Key Vault, or HashiCorp Vault. These services are designed to securely store and manage sensitive information, including API keys, passwords, and certificates.
Integration: These services typically offer SDKs or APIs that you can use from your Python script to retrieve secrets at runtime, minimizing the exposure of sensitive information. 4. Prompt for Credentials If the script is intended for manual, infrequent use, you might simply prompt the user to enter their email credentials when the script runs. This approach avoids storing the credentials but requires manual input each time.
python Copy code email_user = input("Enter your email user: ") email_password = input("Enter your email password: ") Considerations Always use secure connections (SSL/TLS) when transmitting sensitive information, such as when sending emails. Regularly review who has access to the environment variables, configuration files, or secret management systems that store sensitive credentials. Implement access controls and auditing to monitor the use of sensitive information. Each method has its context where it shines, so choose the one that best fits your security requirements and operational environment. If you're deploying scripts in a shared or production environment, secret management services or environment variables are generally the preferred approaches.
Thanks for the primer on the various methods of protecting credentials, it was very informative. Now I have a question about some of the syntax in the email function. On the following line, it has what looks like a place for you to hard-code your email address and then it has the variable ADMIN_EMAIL. What is the function of the [email protected] in this line of code? "server.sendmail('[email protected]', ADMIN_EMAIL, text)"
In the server.sendmail('[email protected]', ADMIN_EMAIL, text) call within the email function, the '[email protected]' represents the email address of the sender, while ADMIN_EMAIL is the recipient's email address. This line of code is telling the SMTP server that an email should be sent from '[email protected]' to the email address stored in the ADMIN_EMAIL variable, with the content described by text.
Here's a breakdown of the parameters in the sendmail method:
First parameter ('[email protected]'): This is the sender's email address. In an actual implementation, you would replace '[email protected]' with the email address you're using to send the email. This should match or be authorized by the SMTP server you're logging into. Some SMTP servers validate the sender's address against the logged-in session and might reject the email if there's a mismatch.
Second parameter (ADMIN_EMAIL): This is the recipient's email address, where the email will be sent. In your script, you've set up ADMIN_EMAIL as a global variable that can be configured to specify the administrator's email address. This flexibility allows the script to notify different administrators without changing the script's source code directly.
Third parameter (text): This is the email's content, including headers and the body, formatted as a string. In the context of your script, it's constructed from a MIMEMultipart object that allows for more complex email structures, including attachments or HTML content if needed.
The reason for explicitly specifying the sender's email address in the sendmail call, even though you're already logged into the SMTP server with credentials, is to define the "From" field in the email itself. SMTP authentication is about proving to the server that you're allowed to send emails, while the "From" address in the sendmail method is about telling the server and the recipient how the email should be labeled in terms of its origin.
For clarity and to align with best practices, you'd typically use the same email address for the "From" parameter in sendmail that you use for logging into the SMTP server. This consistency helps avoid issues with email delivery, such as being flagged as spam due to SPF (Sender Policy Framework) checks or other security measures implemented by email providers.
When implementing this in a real-world scenario, ensure that the email address and corresponding login credentials are securely managed, as discussed in the strategies for protecting sensitive configuration data.
something interesting has happened, I think as a result of the addition of the log limiting code. Now every record is being logged twice. It would seem that we are calling for each line twice by calling a/the logging method twice? Here is the script, see if you can see where this is happening. "# Use this file to scan directory trees for sensitive information prior to committing to a public repo.
Once you know where the info is, you can correct the necessary issues by changing or removing the data
import os import re import logging from logging.handlers import RotatingFileHandler from datetime import datetime import smtplib from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart
LOG_FILE_NAME = 'sensitive_data_scan.log'
ADMIN_EMAIL = '' SENDERS_EMAIL = '[email protected]' SENDERS_PWORD = ''
logging.basicConfig(filename=LOG_FILE_NAME, level=logging.INFO, format='%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
log_handler = RotatingFileHandler(LOG_FILE_NAME, maxBytes=210241024, backupCount=5) log_handler.setFormatter(logging.Formatter('%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S'))
logger = logging.getLogger() logger.setLevel(logging.INFO) logger.addHandler(log_handler)
regex_patterns = { # Your patterns here 'API Key': r'(?i)(apikey|api_key|access_key|secret_key)\s*[:=]\s*\S+', 'Email Address': r'[\w.-]+@[\w.-]+.\w+', 'URL': r'https?://\S+', 'SSH Public Key': r'ssh-(rsa|ed25519)|ecdsa-sha2-nistp[0-9]+', 'SSH Private Key': r'-----BEGIN (RSA|DSA|EC|OPENSSH) PRIVATE KEY-----', # Matches strings of hex characters that are at least 16 chars long 'Hex Encoded Blob': r'(?:[0-9a-fA-F]{2}){16,}', # Additional patterns as needed }
def log_scan_result(filepath, line_num=None, data_type=None): if line_num and data_type: logging.info(f'Potential {data_type} found in {filepath} on line {line_num}') else: logging.info(f'Scanned {filepath}: Clean')
def find_sensitive_info(root_dir):
# Add the log file path
log_file_path = os.path.join(root_dir, 'sensitive_data_scan.log')
for dirpath, dirnames, filenames in os.walk(root_dir):
for filename in filenames:
filepath = os.path.join(dirpath, filename)
# Check for and skip the log file
if filepath.endswith(LOG_FILE_NAME):
continue
found_sensitive_info = False
try:
with open(filepath, 'r', errors='ignore') as file:
for i, line in enumerate(file, 1):
for data_type, pattern in regex_patterns.items():
if re.search(pattern, line, re.IGNORECASE):
log_scan_result(filepath, i, data_type)
found_sensitive_info = True
except Exception as e:
logging.error(f'Error scanning file {filepath}: {e}')
if not found_sensitive_info:
log_scan_result(filepath)
def send_email(subject, body): if ADMIN_EMAIL: msg = MIMEMultipart() msg['From'] = SENDERS_EMAIL msg['To'] = ADMIN_EMAIL msg['Subject'] = subject
msg.attach(MIMEText(body, 'plain'))
# Update with your SMTP server details
server = smtplib.SMTP('smtp.example.com', 587)
server.starttls()
# Your login credentials from Global Variables above
server.login(SENDERS_EMAIL, SENDERS_PWORD)
text = msg.as_string()
# Senders and Admin email addresses from Global Variables above
server.sendmail(SENDERS_EMAIL, ADMIN_EMAIL, text)
server.quit()
def main(root_directory): # capture the start_time for logging to console start_time = datetime.now() # execute the scan find_sensitive_info(root_directory) # capture the end_time for logging to console end_time = datetime.now() # calculate duration for logging to console duration = end_time - start_time # craft the completion message completion_message = f"Script completed successfully in {duration.total_seconds()} seconds." # log message to console to inform user of completion and duration print(completion_message) # log message to file for durable record logging.info(completion_message) # Send email notification if ADMIN_EMAIL is set send_email("Sensitive Info Scan Completed", completion_message)
if name == 'main': # Insert a visual break/new scan header in the log file logging.info("\n\n----- New scan started on {} -----".format(datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
# Query user for path to directory to scan
root_directory = input("Enter the directory path to scan: ")
# Call the main function to execute the scan
main(root_directory)
"
The issue you're encountering with each log message being duplicated likely stems from the combination of the default logger setup via logging.basicConfig and the additional logger configuration through the RotatingFileHandler. When you configure logging using basicConfig and then add another handler to the default logger, both handlers end up processing and logging the messages. That's why you're seeing each entry twice in your log file.
To fix this, you should remove the basicConfig call and rely solely on your RotatingFileHandler setup to configure the logger. Here's how you can adjust your script:
python Copy code import os import re import logging from logging.handlers import RotatingFileHandler from datetime import datetime import smtplib from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart
LOG_FILE_NAME = 'sensitive_data_scan.log' ADMIN_EMAIL = '' # Set this to the admin's email, or leave it as '' to disable email notifications SENDERS_EMAIL = '[email protected]' SENDERS_PWORD = ''
log_handler = RotatingFileHandler(LOG_FILE_NAME, maxBytes=210241024, backupCount=5) log_handler.setFormatter(logging.Formatter('%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S'))
logger = logging.getLogger() logger.setLevel(logging.INFO) logger.addHandler(log_handler)
Adjust your log_scan_result function and other logging calls to use this logger instead of the root logger
def log_scan_result(filepath, line_num=None, data_type=None): if line_num and data_type: logger.info(f'Potential {data_type} found in {filepath} on line {line_num}') else: logger.info(f'Scanned {filepath}: Clean') By making these changes, you're ensuring that all log messages go through the RotatingFileHandler you configured, and you're avoiding the default logging configuration which was causing the duplication. Remember to also adjust any other logging calls in your script to use logger instead of the root logger directly (logging.info, logging.error, etc., should be changed to logger.info, logger.error, etc.).
This approach centralizes your logging configuration to the RotatingFileHandler, which is set up to handle the file rotation based on the file size you've specified, and removes the redundancy in logging setup that led to the duplicated log entries.
That wasn't it. We're going to have to refactor all of the logging functionality. What can I do to help?
To streamline and correct the logging functionality in your script, we'll go through a refactoring process that ensures logs are handled efficiently and without duplication. Here’s a step-by-step guide to refactor the logging setup:
Step 1: Refactor Logging Setup First, ensure that the logger is configured only once, and it's done in a way that doesn't conflict with any default configurations. Since we're using a RotatingFileHandler, we'll stick with that and remove any basicConfig usage to avoid double-handling.
Step 2: Use a Specific Logger Using a specific logger (rather than the root logger) can help control the logging more granularly. We'll name this logger and use it throughout the script.
Step 3: Adjust Logging Calls Replace direct calls to the logging module (like logging.info(), logging.error()) with calls to our specific logger instance.
Let’s implement these steps:
python Copy code import os import re import logging from logging.handlers import RotatingFileHandler from datetime import datetime import smtplib from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart
logger = logging.getLogger('SensitiveInfoFinder') logger.setLevel(logging.INFO) # Set the logging level
log_file_handler = RotatingFileHandler('sensitive_data_scan.log', maxBytes=210241024, backupCount=5) log_file_handler.setFormatter(logging.Formatter('%(asctime)s %(message)s', datefmt='%Y-%m-%d %H:%M:%S')) logger.addHandler(log_file_handler)
ADMIN_EMAIL = '' SENDERS_EMAIL = '[email protected]' SENDERS_PWORD = ''
regex_patterns = { 'API Key': r'(?i)(apikey|api_key|access_key|secret_key)\s*[:=]\s*\S+', 'Email Address': r'[\w.-]+@[\w.-]+.\w+', 'URL': r'https?://\S+', 'SSH Public Key': r'ssh-(rsa|ed25519)|ecdsa-sha2-nistp[0-9]+', 'SSH Private Key': r'-----BEGIN (RSA|DSA|EC|OPENSSH) PRIVATE KEY-----', 'Hex Encoded Blob': r'(?:[0-9a-fA-F]{2}){16,}', }
def log_scan_result(filepath, line_num=None, data_type=None): message = f'Potential {data_type} found in {filepath} on line {line_num}' if line_num and data_type else f'Scanned {filepath}: Clean' logger.info(message)
The rest of your functions (find_sensitive_info, send_email) remain largely the same, but ensure any logging calls use 'logger'
def main(root_directory): start_time = datetime.now() find_sensitive_info(root_directory) end_time = datetime.now() duration = end_time - start_time completion_message = f"Script completed successfully in {duration.total_seconds()} seconds." print(completion_message) logger.info(completion_message) if ADMIN_EMAIL: send_email("Sensitive Info Scan Completed", completion_message)
if name == 'main': logger.info("\n\n----- New scan started on {} -----".format(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))) root_directory = input("Enter the directory path to scan: ") main(root_directory) This refactoring centralizes the logging setup to use a single RotatingFileHandler attached to a specific logger. By directing all log messages through this logger, you control the output more precisely and eliminate the issue of duplicate log entries.
Ensure that any logging inside find_sensitive_info, send_email, or any other function uses logger instead of the root logging module. This change should resolve the duplicate logging issue and maintain the functionality of limiting the log file size.
If there's anything more you need help with or further adjustments required, feel free to ask!
You know, after reading through your work, I just commented out two lines of code and got the same result. It really was all about the logging.basicConfig() function. So I chose to leave everything else as is, and truthfully you only changed one function and I just thing the old version is more human readable on only costs 11 bits more in space and processing power. So for my needs, I think I have that problem solved. Thanks for assisting in that experience. I couldn't have written this script in a couple of days without your help, much less, a couple of hours.
I'm thrilled to hear you found a solution that works for you, and it was a pleasure to assist in the journey of developing your script! Simplifying the approach by adjusting or removing specific lines, like the logging.basicConfig() call, often leads to efficient and effective solutions. It's a great reminder of the power of iteration and refinement in coding projects.
Remember, the path to creating useful tools is filled with learning, experimenting, and sometimes simplifying. Your willingness to explore different solutions and adapt is commendable.
If you ever need further assistance, whether it's diving back into this project or embarking on a new adventure, don't hesitate to reach out. Happy coding, and best of luck with your script and future projects!
I do want to address one more thing before we wrap it up for the night, as it is getting late for me here. I would like to make it optional to accept the current folder where the script is being executed as the default location to perform the scan or allow the user to put in a relative file path to any directory on the machine, (and then my mind went totally crazy and I thought, any directory, on any computer, anywhere, for which the user has credentials. I mean we do live in a networked/remote-work world, am I right?
Making your script flexible in terms of the location it scans, including providing a default option to scan the current directory, specifying a relative path, or even scanning directories on remote machines, is a fantastic way to enhance its usability and adaptability. Let's tackle these enhancements step by step.
Accepting a Default or Relative Path First, we can modify the script to use the current directory as a default scan location or allow the user to input a relative path. This can be achieved by modifying how you prompt for the directory path:
python Copy code if name == 'main': logger.info("\n\n----- New scan started on {} -----".format(datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
# Prompt the user for a directory path or use the current directory as the default
root_directory = input("Enter the directory path to scan (leave blank to scan the current directory): ").strip()
if not root_directory: # If the user enters an empty string, use the current directory
root_directory = os.getcwd() # Get the current working directory
main(root_directory)
This approach lets the user either specify a path or simply press Enter to use the current directory, enhancing the script's flexibility.
Scanning Directories on Remote Machines Expanding the script's capability to scan directories on remote machines introduces a significant level of complexity, mainly due to the need to handle network communication and authentication securely. For remote scanning, consider the following options based on the environment and available tools:
SSH for Linux/Unix systems: If you have SSH access to the remote machine, you could potentially use SSH to execute the script remotely or to mount the remote filesystem locally (e.g., using SSHFS) and scan it as if it were a local directory. This approach requires careful handling of SSH keys and permissions.
Network File Systems: In some environments, directories on remote machines are accessible via network file systems (e.g., NFS, SMB/CIFS). If properly configured and secure, your script could scan these directories directly.
Cloud and API-based Solutions: For systems hosted in cloud environments, you might leverage cloud storage APIs or management APIs to access and scan files. This approach would require integrating with the cloud provider's SDK and handling authentication securely, typically via API tokens or service accounts.
Distributed Script Execution: Another approach is to have the script itself or a version of it reside on and be executed directly on the remote machines, with results reported back to a central server or administrator. This could be orchestrated using configuration management tools like Ansible, Chef, or Puppet.
Each of these options has its own set of requirements, complexities, and security considerations. For scanning across networks or on remote machines, start with the most secure, straightforward, and efficient method available to you based on the network environment and the systems you're working with.
Implementing remote scanning functionality effectively requires a good understanding of network protocols, security (e.g., SSH key management, secure API usage), and potentially cloud services. Always prioritize security, especially when dealing with potentially sensitive information or accessing remote systems.
I'm glad we could explore these possibilities, and I hope these suggestions inspire further enhancements to your script! If you have more questions or need clarification on any point, feel free to ask.
is the os.getcwd the only way to extract the filepath? That command goes all the way back to / and it exposes more of the filepath than is necessary in the log.
If you're looking to refine how you capture and display file paths in your logs, especially to avoid revealing more of the file path than necessary, you have a couple of options to consider. The goal is to present paths in a way that is informative yet respects privacy and security considerations. Here are some strategies:
- Relative Paths You can display paths relative to a certain base directory, such as the current working directory or another specified directory. This can make the logs more readable and less cluttered with unnecessary path information.
For instance, if you want to display paths relative to the directory where the scan was initiated, you could modify how you log file paths:
python Copy code import os
def log_scan_result(base_dir, filepath, line_num=None, data_type=None): # Make the file path relative to the base directory relative_path = os.path.relpath(filepath, start=base_dir) message = f'Potential {data_type} found in {relative_path} on line {line_num}' if line_num and data_type else f'Scanned {relative_path}: Clean' logger.info(message) Here, os.path.relpath() is used to compute the relative filepath from base_dir to filepath. This means that if filepath is /home/user/projects/myproject/file.txt and base_dir is /home/user/projects/myproject, the relative path would simply be file.txt.
- Custom Path Truncation If you have specific parts of the path you wish to always hide or if the relative path solution doesn't fully meet your needs, you could implement a custom function to truncate or modify the file paths according to your rules before logging them.
This approach requires you to define exactly how you want to manipulate the path strings to achieve the desired logging output.
Using os.getcwd() Alternatives os.getcwd() fetches the current working directory, which is indeed the full path to the directory from which your Python script is executed. If your concern is about minimizing the amount of path information exposed in the logs:
Using os.path.relpath(): As shown above, you can use os.path.relpath() to convert absolute paths to relative paths based on a given start point, which could be the directory where the script is located (os.path.dirname(file)) or another base directory defined by the script's logic.
Current Script Directory: If you're looking to base paths off the script's location, os.path.dirname(file) can provide the directory containing the script, which might be useful if the script and the files it operates on are in the same or closely related directories.
Remember, the best approach depends on your specific requirements for logging and how you prefer to represent file paths in your logs. Adjusting the way paths are logged can help make the logs more useful to users while protecting sensitive directory structure information.
While attempting to run your recommended code, I am encountering this particular issue. Here is the error code from the console. Tell me what you see. "carlos@Carloss-MacBook-Pro admin_automation % python3 sensitive_data_scan.py
Enter the directory path to scan (leave blank to scan the current directory): Traceback (most recent call last): File "/Users/carlos/Sites/admin_automation/sensitive_data_scan.py", line 131, in main(root_directory) File "/Users/carlos/Sites/admin_automation/sensitive_data_scan.py", line 102, in main find_sensitive_info(root_directory) File "/Users/carlos/Sites/admin_automation/sensitive_data_scan.py", line 78, in find_sensitive_info log_results(filepath) TypeError: log_results() missing 1 required positional argument: 'filepath'"
The error message you're seeing, TypeError: log_results() missing 1 required positional argument: 'filepath', suggests that the log_results function is being called without the correct number of arguments. Specifically, it's expecting at least one more argument ('filepath') than is being provided in the call.
Given the context and the functions we discussed earlier, it seems like there might have been a misinterpretation or typo in the function name or its usage. Based on the previous conversation, the function intended for logging scan results is log_scan_result (not log_results), and it requires multiple arguments to work correctly, including at least a 'filepath' and potentially 'line_num' and 'data_type' depending on what you're logging.
Here's a quick recap of how log_scan_result should be defined and used, including adjustments for logging relative paths:
Correct Function Definition and Use Ensure your log_scan_result function looks something like this:
python Copy code def log_scan_result(base_dir, filepath, line_num=None, data_type=None): # Make the file path relative to the base directory relative_path = os.path.relpath(filepath, start=base_dir) message = f'Potential {data_type} found in {relative_path} on line {line_num}' if line_num and data_type else f'Scanned {relative_path}: Clean' logger.info(message) And when calling it within find_sensitive_info, you should pass the necessary arguments:
python Copy code log_scan_result(root_dir, filepath, i, data_type) Or, if no sensitive data is found and you're logging a clean scan:
python Copy code log_scan_result(root_dir, filepath) Fixing the Error The error indicates that there's likely a call to log_results (which seems to be a typo or a different function) somewhere in your find_sensitive_info function, and it's missing the required arguments.
First, ensure you're calling the correct function name: log_scan_result instead of log_results.
Second, verify that you're passing the correct number of arguments whenever you call log_scan_result. At minimum, you need to pass base_dir and filepath. Optionally, you may also include line_num and data_type for detailed logs.
Correct these issues based on your script's logic and intended functionality. If log_results is indeed a different function you intended to use, you'll need to adjust its definition or its calls to match in terms of arguments.
If there's any confusion or further issues arise, feel free to share more detailed code snippets or questions!
Sorry, I did change the name of my function to log_results because it was shorter and I didn't think the other name was particularly more informative. So that being said, I made all the adjustments in the code but in your code snippets above from the find_sensitive_info(), which is still the name, you indicate one of the arguments being 'root_dir', but then in the instructions "Fixing the Error", you reference 'base_dir'. I feel like it should be base_dir in those instances or it should be root_dir in the log_results()/log_scan_result(). Thoughts?
You're correct, and I appreciate your attention to detail. The consistency in naming between root_dir and base_dir is crucial for clarity and to avoid confusion. The discrepancy in my previous messages was unintentional and could indeed lead to misunderstandings.
Given your current setup and naming conventions, it's best to choose one term and stick with it throughout the script to maintain consistency. If you've been using root_dir to refer to the directory being scanned, then it's sensible to use root_dir across your logging function and its calls as well. This ensures that the terminology remains clear and consistent throughout your code.
Here's how you could structure the log_results (or log_scan_result) function and its calls with consistent naming:
Updated Function Definition Using root_dir python Copy code def log_results(root_dir, filepath, line_num=None, data_type=None): # Make the file path relative to the root directory relative_path = os.path.relpath(filepath, start=root_dir) message = f'Potential {data_type} found in {relative_path} on line {line_num}' if line_num and data_type else f'Scanned {relative_path}: Clean' logger.info(message) Corrected Function Call in find_sensitive_info When calling log_results within your find_sensitive_info function, make sure to use root_dir as the first argument, for consistency:
python Copy code log_results(root_dir, filepath, i, data_type) Or, for logging a clean file without sensitive data:
python Copy code log_results(root_dir, filepath) This approach keeps the argument names consistent, making your code more readable and easier to maintain. The use of root_dir as the base directory against which relative paths are calculated helps clarify that this directory is the reference point for all path computations within your script.
By aligning the terminology across your script, you're enhancing its readability and reducing potential points of confusion, both for yourself and others who may work with or review your code in the future.
If you have any more adjustments or questions, feel free to ask. Your thoughtful approach to refining your script is commendable!
Alrighty then, that fixed it. I stuck with the root_dir since it had been a standard throughout the script previously. I can't believe you introduced that erroneous parameter into our otherwise amazing script! Oh well, I forgive you, because it's working again and I got the modification I was hoping for. So, thank you again.
Thank you for your understanding and patience! In the realm of coding, even small discrepancies like the one between root_dir and base_dir can lead to head-scratching moments. I'm glad to hear that sticking with root_dir brought everything back in line and that your script is functioning as you hoped. Your ability to adapt and refine the script through our collaboration has been impressive!
If you have any more ideas, questions, or future projects you'd like to tackle, feel free to reach out. Your ventures into scripting and automation have been a pleasure to assist with, and I'm here to support your next coding endeavor or to help solve any puzzles that come your way. Happy coding, and best of luck with your continued development and deployment of your script!
And yes, I had forgotten that we were trying to modify the way the file paths were being written into the log. That is now closer to the original than after we made it optional to just hit enter to use the cwd or pwd as the root dir for the scan. It's back to normal except with the added functionality so again, we win. I started this project to protect my secrets from Github, but as the script evolved and became more functional, my sysadmin hat went back on and we added so much functionality. That being said, the script detects a lot of .git files which are hidden to anyone except git/Github or the owner, so I'm wondering if we should add a regex to filter those out of the scan. Here are the results of the most recent run of the scan so you can see what I mean. This is the first time you've seen the results of this collaboration! "----- New scan started on 2024-03-29 22:24:03 ----- 2024-03-29 22:24:04 Scanned .DS_Store: Clean 2024-03-29 22:24:04 Scanned gitignore_cheatsheet.md: Clean 2024-03-29 22:24:04 Potential Email Address found in sensitive_data_scan.py on line 18 2024-03-29 22:24:04 Potential URL found in playbooks/roles/apt/tasks/main.yml on line 15 2024-03-29 22:24:04 Potential URL found in playbooks/roles/apt/tasks/main.yml on line 18 2024-03-29 22:24:04 Scanned playbooks/roles/apt/handlers/main.yml: Clean 2024-03-29 22:24:04 Potential URL found in build_docker_ansible_client/vars.yml on line 7 2024-03-29 22:24:04 Potential URL found in build_docker_ansible_client/vars.yml on line 8 2024-03-29 22:24:04 Potential URL found in build_docker_ansible_client/vars.yml on line 9 2024-03-29 22:24:04 Potential URL found in build_docker_ansible_client/vars.yml on line 10 2024-03-29 22:24:04 Potential URL found in build_docker_ansible_client/Dockerfile.yml on line 2 2024-03-29 22:24:04 Potential URL found in build_docker_ansible_client/docker-compose.yml on line 5 2024-03-29 22:24:04 Scanned build_docker_ansible_client/server-config.yml: Clean 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 4 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 7 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 10 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 13 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 16 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 19 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 26 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 29 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 32 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 35 2024-03-29 22:24:04 Potential Hex Encoded Blob found in guides/system_setup.md on line 35 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 46 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 58 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 65 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 108 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 111 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 127 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 132 2024-03-29 22:24:04 Potential Hex Encoded Blob found in guides/system_setup.md on line 156 2024-03-29 22:24:04 Potential URL found in guides/system_setup.md on line 157 2024-03-29 22:24:04 Scanned guides/navigating_proxmox.md: Clean 2024-03-29 22:24:04 Potential URL found in guides/nix_uninstall.md on line 5 2024-03-29 22:24:04 Potential URL found in guides/nix_uninstall.md on line 114 2024-03-29 22:24:04 Potential URL found in guides/nix_uninstall.md on line 116 2024-03-29 22:24:04 Potential URL found in guides/nix_uninstall.md on line 137 2024-03-29 22:24:04 Potential URL found in guides/nix_uninstall.md on line 139 2024-03-29 22:24:04 Potential URL found in guides/dch_error_info.md on line 54 2024-03-29 22:24:04 Potential URL found in guides/colima_via_nix.md on line 21 2024-03-29 22:24:04 Potential URL found in guides/colima_via_nix.md on line 40 2024-03-29 22:24:04 Scanned guides/proxmox_roadmap.md: Clean 2024-03-29 22:24:04 Scanned guides/.gitignore: Clean 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 286 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 287 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 290 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 293 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 309 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 310 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 311 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 312 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 360 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 376 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 379 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 443 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 446 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 479 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 482 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 527 2024-03-29 22:24:04 Potential URL found in guides/docker_ansible_client.md on line 530 2024-03-29 22:24:05 Potential URL found in guides/docker_ansible_client.md on line 667 2024-03-29 22:24:05 Scanned guides/path_array_contents.md: Clean 2024-03-29 22:24:05 Potential URL found in guides/terraform install.md on line 6 2024-03-29 22:24:05 Potential URL found in guides/terraform install.md on line 8 2024-03-29 22:24:05 Potential Email Address found in guides/terraform install.md on line 28 2024-03-29 22:24:05 Potential Email Address found in guides/terraform install.md on line 37 2024-03-29 22:24:05 Scanned scripts/network_config.sh: Clean 2024-03-29 22:24:05 Scanned scripts/nix_uninstall.sh: Clean 2024-03-29 22:24:05 Potential SSH Private Key found in scripts/samba_id_rsa on line 1 2024-03-29 22:24:05 Scanned scripts/.DS_Store: Clean 2024-03-29 22:24:05 Scanned scripts/generate_ssh_key.sh: Clean 2024-03-29 22:24:05 Potential URL found in scripts/oi-mac-installer.sh on line 15 2024-03-29 22:24:05 Potential URL found in scripts/samba.conf on line 8 2024-03-29 22:24:05 Potential URL found in scripts/samba.conf on line 12 2024-03-29 22:24:05 Potential SSH Public Key found in scripts/samba_id_rsa.pub on line 1 2024-03-29 22:24:05 Potential URL found in scripts/proxmox_config.sh on line 95 2024-03-29 22:24:05 Potential URL found in scripts/proxmox_config.sh on line 104 2024-03-29 22:24:05 Potential URL found in scripts/proxmox_config.sh on line 105 2024-03-29 22:24:05 Potential URL found in scripts/proxmox_config.sh on line 106 2024-03-29 22:24:05 Potential URL found in scripts/proxmox_config.sh on line 107 2024-03-29 22:24:05 Potential URL found in scripts/proxmox_config.sh on line 137 2024-03-29 22:24:05 Potential Email Address found in scripts/samba_config.sh on line 11 2024-03-29 22:24:05 Potential Email Address found in scripts/samba_config.sh on line 12 2024-03-29 22:24:05 Potential Email Address found in scripts/samba_config.sh on line 14 2024-03-29 22:24:05 Potential Email Address found in scripts/samba_config.sh on line 15 2024-03-29 22:24:05 Potential URL found in scripts/terraform_install.sh on line 54 2024-03-29 22:24:05 Potential URL found in scripts/terraform_install.sh on line 62 2024-03-29 22:24:05 Potential URL found in .git/config on line 9 2024-03-29 22:24:05 Scanned .git/HEAD: Clean 2024-03-29 22:24:05 Scanned .git/description: Clean 2024-03-29 22:24:05 Scanned .git/index: Clean 2024-03-29 22:24:05 Scanned .git/COMMIT_EDITMSG: Clean 2024-03-29 22:24:05 Scanned .git/objects/3b/16e0c75c887c049d4b661617531b073ec57767: Clean 2024-03-29 22:24:05 Scanned .git/objects/56/57f1d685cc387ae6e277c89384258088019293: Clean 2024-03-29 22:24:05 Scanned .git/objects/3d/6249947495c2d214297977a6e34c6a686c4b45: Clean 2024-03-29 22:24:05 Scanned .git/objects/93/bd70aef35065e3b89884852562b6693dd93609: Clean 2024-03-29 22:24:05 Scanned .git/objects/be/aeee697fdfce8607f408ff114425d8197d0b9b: Clean 2024-03-29 22:24:05 Scanned .git/objects/ae/7c9f001b13e17d49f954da99ceae05181cea7e: Clean 2024-03-29 22:24:05 Scanned .git/objects/e2/bd85d1e677be7fdd10af65fc53410aaa956bfd: Clean 2024-03-29 22:24:05 Scanned .git/objects/c7/16bd6c2665af6b17427cac23776772c857d8f6: Clean 2024-03-29 22:24:05 Scanned .git/objects/c7/2b4f4b7fa974dbb031c9d9923143bcd90fa7da: Clean 2024-03-29 22:24:05 Scanned .git/objects/c0/deaff93cb5dc04006c3e99f88e4408ee433590: Clean 2024-03-29 22:24:05 Scanned .git/objects/c6/e17234c12533111a97392ea28c45e5abbfba22: Clean 2024-03-29 22:24:05 Scanned .git/objects/c6/403a7fe7094c70dd743adbd3f251f60ea7e796: Clean 2024-03-29 22:24:05 Scanned .git/objects/11/68b34180c205d912da63e57fefb574484ad824: Clean 2024-03-29 22:24:05 Scanned .git/objects/29/0c9564298d016805ed247684887658b1a2ce16: Clean 2024-03-29 22:24:05 Scanned .git/objects/42/a424ffe58cd924717814bf056f8e3ecb7d88a5: Clean 2024-03-29 22:24:05 Scanned .git/objects/1f/fb1a8cd11584d53ccac5243e7bd0b897c1857b: Clean 2024-03-29 22:24:05 Scanned .git/objects/87/9c762d0f5fdf86c9a984f372da049aa52dd50e: Clean 2024-03-29 22:24:05 Scanned .git/objects/7e/e24ea82a058aec2562b94158a6e89ab221d3ae: Clean 2024-03-29 22:24:05 Scanned .git/objects/19/472f8abeaf30602dc39eefface84838def0be1: Clean 2024-03-29 22:24:05 Scanned .git/objects/21/1e524f78488554dae5db7c3d693e91a9447cec: Clean 2024-03-29 22:24:05 Scanned .git/objects/86/4f1d4ac7e8ad07500ff8d618270d928feb690f: Clean 2024-03-29 22:24:05 Scanned .git/objects/72/199c5fef93506ff663b45934662786c31f7487: Clean 2024-03-29 22:24:05 Scanned .git/objects/88/0d7e33ec5b95b4fdc7fef7572f14417abf2109: Clean 2024-03-29 22:24:05 Scanned .git/objects/6b/fe3195be21834eaf456a8cee1fc3f0130f11a2: Clean 2024-03-29 22:24:05 Scanned .git/objects/31/087c453f0f1dedf9d3fad36ddf8ec73fad6f37: Clean 2024-03-29 22:24:05 Scanned .git/objects/96/e8bcf959e40446b2755335c750aeae42effd3d: Clean 2024-03-29 22:24:05 Scanned .git/objects/3a/8992f2c798e10b2fbcc858ac46eb585c72878c: Clean 2024-03-29 22:24:05 Scanned .git/objects/99/33560bc7120da3d583f25e82453ec585fd17f2: Clean 2024-03-29 22:24:05 Scanned .git/objects/0f/8f7ab8defc422a04a8c1b19899fbfafb9d6af7: Clean 2024-03-29 22:24:05 Scanned .git/objects/dd/648b61a2a5f1875a22256b81e1251ff7c0b4c4: Clean 2024-03-29 22:24:05 Scanned .git/objects/e0/366c74ecbece8d7da1a16f839c75b11a970f95: Clean 2024-03-29 22:24:05 Scanned .git/objects/12/d04559721eef3272ebff70a20002c2f3ef1fad: Clean 2024-03-29 22:24:05 Scanned .git/objects/12/bbc6c5445a0cc9adf1ecbaf0c35b52c8fedf68: Clean 2024-03-29 22:24:05 Scanned .git/objects/76/d98baf3e0d2a40e79eadeafc0ec0206b7b763f: Clean 2024-03-29 22:24:05 Scanned .git/objects/40/5ae61406664b5087dd81f0dbc078f222b348d9: Clean 2024-03-29 22:24:05 Scanned .git/info/exclude: Clean 2024-03-29 22:24:05 Potential Email Address found in .git/logs/HEAD on line 1 2024-03-29 22:24:05 Potential Hex Encoded Blob found in .git/logs/HEAD on line 1 2024-03-29 22:24:05 Potential Email Address found in .git/logs/refs/heads/master on line 1 2024-03-29 22:24:05 Potential Hex Encoded Blob found in .git/logs/refs/heads/master on line 1 2024-03-29 22:24:05 Scanned .git/hooks/commit-msg.sample: Clean 2024-03-29 22:24:05 Scanned .git/hooks/pre-rebase.sample: Clean 2024-03-29 22:24:05 Scanned .git/hooks/pre-commit.sample: Clean 2024-03-29 22:24:05 Scanned .git/hooks/applypatch-msg.sample: Clean 2024-03-29 22:24:05 Potential URL found in .git/hooks/fsmonitor-watchman.sample on line 8 2024-03-29 22:24:05 Scanned .git/hooks/pre-receive.sample: Clean 2024-03-29 22:24:05 Scanned .git/hooks/prepare-commit-msg.sample: Clean 2024-03-29 22:24:05 Scanned .git/hooks/post-update.sample: Clean 2024-03-29 22:24:05 Scanned .git/hooks/pre-applypatch.sample: Clean 2024-03-29 22:24:05 Potential Hex Encoded Blob found in .git/hooks/pre-push.sample on line 25 2024-03-29 22:24:05 Potential Hex Encoded Blob found in .git/hooks/update.sample on line 63 2024-03-29 22:24:05 Potential Hex Encoded Blob found in .git/refs/heads/master on line 1 2024-03-29 22:24:05 Script completed successfully in 0.243934 seconds."
Based on the scan results you've shared, it's clear that the script is performing well, identifying various types of sensitive data across numerous files. However, as you've noted, .git files and directories, which are integral to Git version control and typically not a concern for exposure in public repositories, are being flagged. To refine the scan and make it more relevant for your needs, excluding .git directories and files from the scan is indeed a sensible approach.
To achieve this, you can incorporate a check in your script to skip scanning any file or directory path that includes .git. This can be done by modifying the find_sensitive_info function where the files and directories are iterated over.
Here's how you might adjust your find_sensitive_info function:
python Copy code def find_sensitive_info(root_dir): for dirpath, dirnames, filenames in os.walk(root_dir): # Skip .git directories dirnames[:] = [d for d in dirnames if d != '.git']
for filename in filenames:
filepath = os.path.join(dirpath, filename)
# Skip any remaining .git related files if they exist outside the standard .git directory
if '.git' in filepath:
continue
found_sensitive_info = False
try:
with open(filepath, 'r', errors='ignore') as file:
for i, line in enumerate(file, 1):
for data_type, pattern in regex_patterns.items():
if re.search(pattern, line, re.IGNORECASE):
log_results(root_dir, filepath, i, data_type)
found_sensitive_info = True
except Exception as e:
logger.error(f'Error scanning file {filepath}: {e}')
if not found_sensitive_info:
log_results(root_dir, filepath)
In this adjustment:
The dirnames[:] = [d for d in dirnames if d != '.git'] line modifies the dirnames list in-place to exclude any .git directories from further traversal. This prevents descending into .git directories during the walk. The if '.git' in filepath: check skips over any files that have .git in their path, including those within .git directories that might have been directly specified or any git-related files that exist outside the standard structure. These changes help focus the script's scanning efforts on files that are more likely to contain unintentionally committed sensitive information, excluding the .git directory and its contents that are generally not exposed in public repositories.
This approach of excluding .git can significantly reduce false positives and make the scan results more actionable for identifying real issues. If there are any more enhancements or questions you have in mind, feel free to share!