Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software
arXiv:2606.20502v1 Announce Type: cross Abstract: Whether LLMs scoring well on vulnerability benchmarks genuinely reason about security or merely pattern-match on contaminated data remains unresolved. We present CWE-Trace, a framework for LLM vulnerability detection built from 834 manually curated Linux kernel samples spanning 74 CWEs. The framework enforces a strict temporal split (pre-2025 historical set / post-cutoff leakage-free set), preserves context-aware vulnerable--patched pairs, and introduces two diagnostic metrics: the Directional Failure Index (DFI) and Hierarchical Distance and D
This research provides a timely and critical evaluation of LLMs' capabilities in software security, surfacing as these models are increasingly integrated into development pipelines.
It directly challenges assumptions about LLM understanding, highlighting potential over-reliance on pattern-matching rather than true reasoning for critical tasks like vulnerability detection in systems software.
The perceived reliability and application boundaries of current LLM-based security tools may need re-evaluation, shifting focus towards robust, transparent diagnostic frameworks.
- · Cybersecurity researchers developing diagnostic tools
- · Developers skilled in traditional security analysis
- · Organizations prioritizing verifiable security guarantees
- · Companies over-relying on LLMs for automated security audits
- · LLM developers without robust testing methodologies
- · Software sectors with complex, low-level codebases
Increased skepticism and more rigorous testing for AI-powered cybersecurity solutions.
A push for LLM architectures that demonstrate explicit reasoning capabilities, not just pattern matching.
Potential for new regulations or industry standards around AI-assisted software security, especially for critical infrastructure.
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Read at arXiv cs.AI