Rethinking CyberSecEval: An LLM-Aided Approach to Evaluation Critique

TL;DR

This paper critically examines Meta's CyberSecEval approach to cybersecurity evaluation, highlighting its limitations, especially in insecure code detection, and demonstrates how LLMs can assist in benchmark analysis.

Contribution

It identifies key limitations in CyberSecEval and showcases the potential of LLMs to improve evaluation critique in cybersecurity benchmarks.

Findings

CyberSecEval has notable limitations in insecure code detection.

LLMs can effectively assist in analyzing and critiquing cybersecurity benchmarks.

The paper proposes improvements for evaluation methodologies using LLMs.

Abstract

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.