Baselines Before Architecture: Evaluating Coding Agents for Autonomous Penetration Testing
Recent autonomous penetration testing papers report high benchmark scores while adding multi-component security harnesses around frontier LLMs. Because these systems often change both architecture and backbone model, it is difficult to tell how much performance comes from the harness rather than from the underlying model. This paper presents a controlled study on the 104-task XBOW benchmark using default coding CLI agents as plain-agent baselines. We first run Codex, OpenCode, and Pi with the same GPT-5 model, budget, target interface, and scoring rule. This phase identifies the strongest same-model baseline and tests whether security-specific prompt variants improve its observed score. W...
| Authors | Ananda Dhakal, Krish Neupane, Aarjan Chaudhary |
|---|---|
| Primary category | cs.CR |
| Published | 13 July 2026 |
| Last updated at source | 13 July 2026 |
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