# Rethinking CyberSecEval: An LLM-Aided Approach to Evaluation Critique

arXiv: https://arxiv.org/abs/2411.08813 · HTML: https://mlsanity.com/paper/2411.08813/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Builds on (most cited first, 3 of 3)

- [Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models](https://mlsanity.com/paper/2312.04724/) (2023)
- [CyberSecEval 2: A Wide-Ranging Cybersecurity Evaluation Suite for Large Language Models](https://mlsanity.com/paper/2404.13161/) (2024)
- [CYBERSECEVAL 3: Advancing the Evaluation of Cybersecurity Risks and Capabilities in Large Language Models](https://mlsanity.com/paper/2408.01605/) (2024)

## Built on by (most cited first, 3 of 3)

- [DarkBench: Benchmarking Dark Patterns in Large Language Models](https://mlsanity.com/paper/2503.10728/) (2025)
- [Give LLMs a Security Course: Securing Retrieval-Augmented Code Generation via Knowledge Injection](https://mlsanity.com/paper/2504.16429/) (2025)
- [Secure Code Generation at Scale with Reflexion](https://mlsanity.com/paper/2511.03898/) (2025)

## Abstract (arXiv)

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.