Rethinking CyberSecEval: An LLM-Aided Approach to Evaluation Critique
This addresses the problem of improving cybersecurity evaluations for researchers and practitioners, but appears incremental as it builds on existing work without claiming major breakthroughs.
The paper critiques Meta's CyberSecEval approach for cybersecurity evaluation, identifying limitations in its insecure code detection, and uses this as a test case for LLM-assisted benchmark analysis, though no concrete results or numbers are provided.
A key development in the cybersecurity evaluations space is the work carried out by Meta, through their CyberSecEval approach. While this work is undoubtedly a useful contribution to a nascent field, there are notable features that limit its utility. Key drawbacks focus on the insecure code detection part of Meta's methodology. We explore these limitations, and use our exploration as a test case for LLM-assisted benchmark analysis.