Cochise: A Reference Harness for Autonomous Penetration Testing

Abstract:Recent work on LLM-driven autonomous penetration testing reports promising results, but existing systems often bundle architectural, prompting, and tool-integration choices together. This makes it difficult to determine what is gained over a simple agent and harness. We present Cochise, a 630 LOC Python reference implementation for autonomous penetration-testing experiments. Cochise connects to a Linux execution host over SSH and supports attacking controlled target environments reachable from that jump host. The prototype implements a Planner--Executor architecture in which long-term state is maintained by the planner, while a ReAct-style executor issues commands over SSH and self-corrects based on command outputs. The scenario prompt can be adapted to different target environments. We evaluate the harness against a live third-party testbed, Game of Active Directory (GOAD). Cochise is intended not as a state-of-the-art penetration-testing agent, but as a reusable experimental infrastructure for comparing models, agent architectures, and penetration-testing traces. Alongside the prototype, we release replay and analysis tools: (i) cochise-replay for offline visualization of captured runs, (ii) cochise-analyze-logs and cochise-analyze-graphs for cost, token, duration, and compromise analysis, and (iii) a corpus of JSON trajectory logs from GOAD runs, so that researchers can study agent behavior without provisioning the 48--64 GB RAM / 190 GB storage testbed themselves. Tool demo video available at this https URL.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)

arXiv:2605.11671 [cs.CR]

(or arXiv:2605.11671v2 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2605.11671 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1145/3832783.3834651 Focus to learn more DOI(s) linking to related resources

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