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Benchmarking Practices in LLM-driven Offensive Security: Testbeds, Metrics, and Experiment Design
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Large Language Models (LLMs) have emerged as a powerful approach for driving offensive penetration-testing tooling. Due to the opaque nature of LLMs, empirical methods are typically used to analyze their efficacy. The quality of this analysis is highly dependent on the chosen testbed, captured metrics and analysis methods employed. This paper analyzes the methodology and benchmarking practices used for evaluating Large Language Model (LLM)-driven attacks, focusing on offensive uses of LLMs in cybersecurity. We review 19 research papers detailing 18 prototypes and their respective testbeds. We detail our findings and provide actionable recommendations for future research, emphasizing the importance of extending existing testbeds, creating baselines, and including comprehensive metrics and qualitative analysis. We also note the distinction between security research and practice, suggesting that CTF-based challenges may not fully represent real-world penetration testing scenarios.
Forward citations
Cited by 5 Pith papers
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LLM pentest agents co-evolved through four bottleneck-driven phases into RLVR systems, while CTF platforms became dual evaluation/training infrastructure and three linked reliability gaps remain.
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On the Surprising Efficacy of LLMs for Penetration-Testing
A critical review arguing that LLMs are surprisingly effective for penetration testing because the task is largely pattern-matching, while noting serious reliability, safety, and cost barriers to autonomous use.
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From Promise to Peril: Rethinking Cybersecurity Red and Blue Teaming in the Age of LLMs
LLMs can assist both attackers and defenders in cybersecurity, but context limits, hallucinations, and weak reasoning make them unsafe to deploy without human oversight and real-world evaluation.
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