Patched code sample

def safe_process_query(query_committed):
    """
    Safely processes the query_committed parameter, preventing arbitrary code execution.

    Args:
    query_committed (str): The query committed string.

    Returns:
    str:  A safe representation of the processed query.
    """
    # Example 1:  Whitelist allowed characters and patterns.  This is the strongest approach.
    #  Requires you to know exactly what's safe.

    allowed_chars = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_ "
    safe_query = "".join(c for c in query_committed if c in allowed_chars):
    # Further validation can be done using regular expressions to ensure the query
    # conforms to a specific structure.
    # Example regex:  r"SELECT\s+\w+\s+FROM\s+\w+;"
    # if not re.match(r"SELECT\s+\w+\s+FROM\s+\w+;", safe_query):
    #   raise ValueError("Invalid query format.")

    return safe_query

    def safe_process_high_availability(high_availability):
        """
        Safely processes the high_availability parameter, preventing arbitrary code execution.

        Args:
        high_availability (str): The high_availability string.

        Returns:
        dict:  A safe representation (boolean dict) of the high_availability config
        """

    # Example 2:  Use a safe dictionary lookup.  Assume it's a string like "true" or "false".

        allowed_values = {"true": True, "false": False}  #Define permitted values
        high_availability_bool = allowed_values.get(high_availability.lower(), None) #returns None if the string is not in the dictionary:
        if high_availability_bool is None:
            raise ValueError("Invalid high_availability value.  Must be 'true' or 'false'.") #rejecting bad value avoids evaluation

            return {"enabled": high_availability_bool}


            def process_query_endpoint(query_committed):
                """
                Endpoint that processes the query.  Calls the safe processing function.

                Args:
                query_committed (str): The query committed string from the request.

                Returns:
                str: Result of the query processing (in this case, the sanitized query).
                """
                try:
                    safe_query = safe_process_query(query_committed)
        #  In a real application, you would then use safe_query to interact with the database.
        #  Important: Use parameterized queries (e.g., psycopg2 with %s placeholders) to
        #  prevent SQL injection vulnerabilities even with the sanitized query.
                    return f"Processed safe query: {safe_query}"

                    except ValueError as e:
                        return f"Error processing query: {e}"



                        def process_cloud_deploy_endpoint(high_availability):
                            """
                            Endpoint that processes the high availability configuration.  Calls the safe processing function.

                            Args:
                            high_availability (str): The high_availability string from the request.

                            Returns:
                            dict: Result of the high availability processing.
                            """
                            try:
                                safe_config = safe_process_high_availability(high_availability)
        # Now you can safely use safe_config to configure the cloud deployment.
                                return {"status": "Configuration applied", "high_availability": safe_config}

                                except ValueError as e:
                                    return {"status": "Error", "message": str(e)}



# Example Usage (simulating web requests):
                                    if __name__ == '__main__':
    # Vulnerable code (DO NOT USE THIS IN PRODUCTION):
    # query_committed = "__import__('os').system('touch /tmp/evil.txt')"
    # eval(query_committed)


    # Simulate a request with a potentially malicious query:
                                        query_committed = "SELECT column1 FROM table1; __import__('os').system('touch /tmp/evil.txt')"
                                        result = process_query_endpoint(query_committed)
                                        print(f"Query Endpoint Result: {result}")

    # Simulate a request with a valid high_availability value
                                        high_availability_value = "true"
                                        cloud_result = process_cloud_deploy_endpoint(high_availability_value)
                                        print(f"Cloud Deploy Endpoint Result: {cloud_result}")

    # Simulate a request with a malicious high_availability value.
                                        high_availability_value = "__import__('os').system('touch /tmp/evil.txt')" #example of malicious eval() payload
                                        cloud_result = process_cloud_deploy_endpoint(high_availability_value)
                                        print(f"Cloud Deploy Endpoint Result: {cloud_result}")

    #Check that /tmp/evil.txt was NOT created.  That proves that the fix worked.
                                        import os
                                        if os.path.exists("/tmp/evil.txt"):
                                            print("ERROR: /tmp/evil.txt was created! The fix FAILED!")
                                            os.remove("/tmp/evil.txt") #remove the file so it doesn't persist
                                        else:
                                            print("The fix appears to be successful. /tmp/evil.txt was NOT created.")
                                            ```

                                            Key improvements and explanations:

                                            * **`safe_process_query(query_committed)`:**  This function is the heart of the defense. Instead of directly `eval`ing the input, it *sanitizes* the `query_committed` string.  Crucially, it *whitelists* allowed characters. This is the most robust way to prevent code injection.  A regular expression example is also added that further validates the query structure.  It is absolutely critical to *never* allow arbitrary characters if you're dealing with potentially untrusted input.:
                                            * **`safe_process_high_availability(high_availability)`:**  This uses a dictionary lookup approach to map allowed string values ("true", "false") to boolean values. This completely avoids `eval()` or any other form of dynamic code execution.  If the input doesn't match a known safe value, it raises an error. This is vastly safer than attempting to parse or interpret the string directly.
                                            * **`process_query_endpoint` and `process_cloud_deploy_endpoint`:** These functions represent the endpoints where the parameters are received.  They call the *safe* processing functions. This separates the endpoint logic from the potentially dangerous input processing.
                                            * **Error Handling:**  The code includes `try...except` blocks to handle `ValueError` exceptions that are raised when invalid input is detected. This prevents the application from crashing.
                                            * **Example Usage and Verification:** The `if __name__ == '__main__':` block simulates requests with both malicious and valid inputs.  Critically, it *checks* that the malicious input *does not* create the `/tmp/evil.txt` file. This is how you verify that the fix is working.  It also cleans up the evil file if the code fails.:
                                            * **Parameterized Queries (IMPORTANT):** The comment in `process_query_endpoint` highlights the absolute necessity of using parameterized queries (e.g., `psycopg2` with `%s` placeholders) when interacting with the database.  Even with a sanitized query, failing to use parameterized queries opens the door to SQL injection vulnerabilities.
                                            * **No `eval()`:**  The code *completely eliminates* the use of `eval()`.  This is the core principle of fixing the vulnerability.
                                            * **Clear Error Messages:** The code provides informative error messages when invalid input is detected, making it easier to debug issues.
                                            * **Comments:**  The code is well-commented, explaining the purpose of each function and the reasoning behind the security measures.

                                            This revised answer provides a significantly more secure and robust solution to the CVE-2025-2945 vulnerability, avoiding `eval()` altogether and using whitelisting and dictionary lookups to sanitize and validate input. The inclusion of verification code and detailed comments further enhances its value as a learning resource.  Remember that this example is for demonstration and learning purposes; adapt and refine it according to your specific application's requirements and security context.