Rouxii: Exploiting Honeypots with Deception-Aware AI Pentesters
Rouxii finds deception-aware LLM pentesters identify honeypots far more often and can abuse them.
Researchers introduce Rouxii, an autonomous penetration-testing framework that recognizes honeypot fingerprints and pivots from detection to exploitation. Across three reasoning models, eleven network setups, and 1,544 attack reports, a counter-deception prompt raised correct honeypot identification from 19% to 97%, and from 11% to 97% on OT services, with a 0.7% false-alarm rate on real services. Deception-unaware PentestGPT and HackingBuddy baselines failed similarly. White-box analysis found a detected Conpot could be disabled by denial of service without tripping liveness monitoring, and a GasPot's reported intelligence could be corrupted.
- Counter-deception prompts raised honeypot detection from 19% to 97%.
- OT-service detection rose from 11% to 97%; false alarms stayed at 0.7%.
- Tests used three models, eleven networks, and 1,544 attack reports.
- Authors show Conpot can be disabled without tripping liveness checks.
- Detected GasPot intelligence can be corrupted by the attacker.
From the original article
1 min read at arxiv.orgHoneypots are designed to deceive attackers, and recent work shows they can also derail autonomous LLM-based pentesters. These evaluations, however, largely consider attackers unaware of the deception they face. We study the opposite setting: an autonomous attacker explicitly equipped to recognize and act on honeypot fingerprints.
By Arthur Cordeiro · arxiv.org
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