Grounded or Gamed? We Audited Our Own Cyber Benchmark

The blog post explores the effectiveness of large language models (LLMs) in detecting Insecure Direct Object Reference (IDOR) vulnerabilities within codebases through a benchmarking study. It evaluates the models using traditional metrics like precision, recall, and F1 score, but introduces additional measures such as groundedness, counterfactual reasoning, metamorphic testing, selectivity, and stability to assess the models' reasoning capabilities. The findings reveal that while none of the models are exploiting the benchmark through pattern matching, they all share a significant weakness in recall, struggling to identify a majority of the vulnerabilities. The study highlights that the models are effective at identifying straightforward vulnerabilities but fail to detect more complex issues, pointing to a common challenge across the field. The post emphasizes that the real frontier in this domain is improving recall to capture the vulnerabilities that currently go unnoticed by all models, including the author's own Semgrep Multimodal agent.

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