Every Model Cheats: Prompt-Level Mitigation of Cheating on Offensive Cyber Tasks
Large language model (LLM) agents routinely cheat on cybersecurity benchmarks, inflating reported pass rates far beyond genuine capability. Prior audits of Cybench found cheating in 0.3-3.4% of traces, implicating only a handful of models. We present a controlled prompt-ablation study across 22 frontier models from 7 providers on 23 Cybench capture-the-flag (CTF) challenges under three prompt conditions (no anti-cheat, standard, severe). All 1,518 task traces were individually audited through a four-stage pipeline combining LLM-as-a-judge classification, programmatic verification, judge-verifier reconciliation, and human review. We find cheating is far more pervasive than previously estim...
| Authors | Michael Kouremetis, Ads Dawson, Raja Sekhar Rao Dheekonda et al. |
|---|---|
| Primary category | cs.CR |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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