# Evaluating LLM Generated Detection Rules in Cybersecurity

arXiv: https://arxiv.org/abs/2509.16749 · HTML: https://mlsanity.com/paper/2509.16749/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 21 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 19 of 19)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Why Do Multi-Agent LLM Systems Fail?](https://mlsanity.com/paper/2503.13657/) (2025)
- [AI Agents That Matter](https://mlsanity.com/paper/2407.01502/) (2024)
- [LLM Agents can Autonomously Hack Websites](https://mlsanity.com/paper/2402.06664/) (2024)
- [SWE-Lancer: Can Frontier LLMs Earn 1 Million from Real-World Freelance Software Engineering?](https://mlsanity.com/paper/2502.12115/) (2025)
- [ReCode: Robustness Evaluation of Code Generation Models](https://mlsanity.com/paper/2212.10264/) (2022)
- [SWE-bench Goes Live!](https://mlsanity.com/paper/2505.23419/) (2025)
- [CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation](https://mlsanity.com/paper/2501.08200/) (2025)
- [Constrained Decoding for Secure Code Generation](https://mlsanity.com/paper/2405.00218/) (2024)
- [Cost-of-Pass: An Economic Framework for Evaluating Language Models](https://mlsanity.com/paper/2504.13359/) (2025)
- [RAS-Eval: A Comprehensive Benchmark for Security Evaluation of LLM Agents in Real-World Environments](https://mlsanity.com/paper/2506.15253/) (2025)
- [When Large Language Models Confront Repository-Level Automatic Program Repair: How Well They Done?](https://mlsanity.com/paper/2403.00448/) (2024)
- [AIRTBench: Measuring Autonomous AI Red Teaming Capabilities in Language Models](https://mlsanity.com/paper/2506.14682/) (2025)
- [A Preliminary Study on the Robustness of Code Generation by Large Language Models](https://mlsanity.com/paper/2503.20197/) (2025)
- [DefenderBench: A Toolkit for Evaluating Language Agents in Cybersecurity Environments](https://mlsanity.com/paper/2506.00739/) (2025)
- [A Practical Guide for Evaluating LLMs and LLM-Reliant Systems](https://mlsanity.com/paper/2506.13023/) (2025)
- [Evaluating and Improving the Robustness of Security Attack Detectors Generated by LLMs](https://mlsanity.com/paper/2411.18216/) (2024)

## Built on by (most cited first, 4 of 4)

- [CTI-REALM: Benchmark to Evaluate Agent Performance on Security Detection Rule Generation Capabilities](https://mlsanity.com/paper/2603.13517/) (2026)
- [SMETA-ZSL:Semantic Meta-Alignment for Zero-Shot Threat Classification](https://mlsanity.com/paper/2607.09936/) (2026)
- [Operationalizing Cyber Threat Intelligence with GraphRAG](https://mlsanity.com/paper/2608.13050/) (2026)
- [Evaluating Agentic Learning Harness Capabilities Without Labels via the Scaling Hypothesis](https://mlsanity.com/paper/2608.13608/) (2026)

## Abstract (arXiv)

LLMs are increasingly pervasive in the security environment, with limited measures of their effectiveness, which limits trust and usefulness to security practitioners. Here, we present an open-source evaluation framework and benchmark metrics for evaluating LLM-generated cybersecurity rules. The benchmark employs a holdout set-based methodology to measure the effectiveness of LLM-generated security rules in comparison to a human-generated corpus of rules. It provides three key metrics inspired by the way experts evaluate security rules, offering a realistic, multifaceted evaluation of the effectiveness of an LLM-based security rule generator. This methodology is illustrated using rules from Sublime Security's detection team and those written by Sublime Security's Automated Detection Engineer (ADE), with a thorough analysis of ADE's skills presented in the results section.