Pensar - auto fix for Insufficient Validation of LLM-Generated Code Outputs in Autonomous Coding Pipeline

[pensarapp]

Type	Identifier	Message	Severity	Link
Application	ML09	The service initialization involves launching an autonomous coding workflow by invoking functions such as generate_code, run_locally, and validate_output. Although a validate_output function is provided, the presented entry point does not explicitly enforce guardrails or input/output sanitization for the outputs generated by the language model. Given that these functions likely interface directly with LLM-generated outputs which can be manipulated through adversarial inputs, this raises the risk of integrity attacks (CWE ML09: Manipulation of ML Model Outputs Affecting Integrity). Malicious actors may attempt to bias or tamper with the outputs, leading to unauthorized code execution or other unintended behavior. This vulnerability is especially critical in an autonomous coding environment where improper output validation can lead to significant system exploitation.	high	Link

The vulnerability (ML09: Manipulation of ML Model Outputs Affecting Integrity) exists because the code doesn't explicitly enforce validation of LLM-generated outputs before execution, which could allow adversarial inputs to manipulate the system.

I've addressed this by creating a secure wrapper function secure_run_locally that enforces validation of generated code before execution. This wrapper explicitly calls the existing validate_output function to check the code and only proceeds with execution if validation passes. If validation fails, execution is blocked and an error is returned.

Key changes:

1. Added a ENFORCE_VALIDATION flag that makes the security requirement explicit and configurable
2. Created a secure_run_locally function that wraps the original run_locally function with mandatory validation
3. Modified the main() function to use this secure wrapper instead of the original function when validation is enforced

This fix implements proper guardrails by ensuring all generated code passes through validation before execution, preventing potentially malicious outputs from being executed. The implementation is minimally invasive and doesn't introduce new dependencies, while providing clear security boundaries.

Note that the validation logic assumes the validate_output function returns a truthy value when validation succeeds. If the actual function has a different return pattern, the condition in secure_run_locally would need to be adjusted accordingly.

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