Security Risk Report: Potential Code Execution via eval() in openai_model.py

[glmgbj233]

File: openai_model.py Method: fc() Relevant Lines: 112-115

Description of Vulnerability
The fc() method in openai_model.py attempts to parse the llm_output (which originates from a Large Language Model) as a JSON string. While it first tries to use json.loads() for this purpose, it falls back to eval() if json.loads() fails. This fallback mechanism introduces a critical security vulnerability.

Vulnerable Code Snippet:

        try:
            function_call_output = json.
            loads(match_value.group(1).strip
            ())
        except:
            function_call_output = eval
            (match_value.group(1).strip())

Impact
The use of eval() with untrusted input (in this case, the output from an LLM, which could potentially be manipulated or generate malicious code) can lead to severe consequences, including:

1. Arbitrary Code Execution: An attacker could craft a malicious payload in the LLM's output that, when eval() uated, executes arbitrary Python code on the server. This could lead to full system compromise.

2. Information Disclosure: Malicious code could be used to read sensitive files, access environment variables, or exfiltrate data from the system.

3. Denial of Service (DoS): An attacker could provide input that causes eval() to execute a computationally expensive operation, leading to resource exhaustion and a denial of service.

4. Sandbox Escape: If the application is running within a sandbox, a successful eval() injection could potentially allow an attacker to break out of the sandbox environment.
   Mitigation Strategies
   To address this vulnerability, the following actions are recommended:

5. Strict JSON Validation :

• Remove eval() fallback: The primary and most effective solution is to completely remove the eval() fallback. If the llm_output is expected to be JSON, it should strictly adhere to JSON format. Any non-JSON output should be treated as an error or handled gracefully without execution.
• Implement robust error handling: If json.loads() fails, log the error and handle the llm_output as invalid, rather than attempting to execute it.

    # ... existing code ...
    try:
        function_call_output = json.loads
        (match_value.group(1).strip())
    except json.JSONDecodeError:
        # Handle invalid JSON output 
        gracefully, e.g., log an error 
        or return a default value
        # DO NOT use eval() here.
        logger.error(f"LLM output is not 
        valid JSON: {match_value.group
        (1).strip()}")
        function_call_output = {}
    # ... existing code ...