Fixing, Breaking, or Faking It? An Execution-Calibrated Evaluation of LLM Vulnerability Patching in JavaScript, Python, Go, and Java, and the Limits of LLM-as-Judge
TL;DR
Large language models (LLMs) increasingly repair software vulnerabilities, but most evaluations judge only similarity to a developer fix or removal of the weakness, leaving no single judge trustworthy.
Abstract
Large language models (LLMs) increasingly repair software vulnerabilities, but most evaluations judge only similarity to a developer fix or removal of the weakness. Neither reveals whether working code was broken. We evaluate eight commercial and open LLMs on 922 JavaScript vulnerability patches, scoring neutralisation and functional preservation. Lacking tests, we score at scale with a reference-based LLM judge, calibrated against execution on a 144-patch benchmark and 254 Java-CVE patches, plus a cross-family judge. The best model fixes 23% of vulnerabilities (judge-based), and cost-efficiency inverts the accuracy ranking. Our central finding concerns the instrument: both judges flag more over-fixes than execution confirms (precision 5–10%), yet on the functional axis agree far more with each other (κ=0.75) than with execution (κ≤0.26), so judge–judge agreement measures reliability, not validity. On real Java code the over-reporting persists, while the judges’ correctness estimates diverge, leaving no single judge trustworthy. Over-fixing is genuine but, under execution, uncommon: a few percent of vulnerability-removing patches, or under 2%, excluding one artefact-prone scenario, both lower bounds. Only adequately tested execution measures the functional-preservation rate, so security-patch evaluation must run the code, use a judge only to rank models, and weigh costs. We release the harness and executable benchmark.
