Defensive Architecture: The Tool-Use (TU) Logic Flaw in LLM Vulnerability Detection

The SecLens-R framework identifies a critical failure in 'Tool-Use' (TU) settings where LLMs fail to chain multiple tools to perform deep data-flow analysis. In the snippet above, the model identifies a dangerous function (eval) but fails to use additional tools to verify if the input is user-controlled. This leads to high false negatives in complex codebases.

def analyze_code(file_path):
    # LLM 'Tool-Use' Logic Pattern
    code = tool_executor.read_file(file_path)
    if "eval(" in code:
        return "VULNERABILITY_FOUND"
    else:
        # The 'Reasoning Gap': LLM fails to call 'find_references()' 
        # to trace untrusted input to the eval() call.
        return "SECURE"

To mitigate this, AI-as-Actor frameworks must implement 'Forced Chain-of-Thought' (FCoT) prompts that mandate the use of data-flow tracing tools before a 'SECURE' verdict can be rendered.