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This research introduces "Know Your Agent" (KYA), a framework that formalizes reconnaissance-driven pentesting specifically tailored for AI agents to counter indirect prompt injection attacks. By automating black-box probing and building target profiles, the study provides critical benchmarks and tools essential for hardening autonomous systems against evolving adversarial strategies. The work bridges machine learning security with traditional cybersecurity methodologies, offering actionable insights for securing next-generation agent architectures.
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Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents

arXiv2 min read

arXiv:2607.19837v1 Announce Type: new Abstract: Traditional pentesting uses reconnaissance at each step to uncover unseen weaknesses, build stronger attacks, and advance the objective; we argue that AI agents require the same treatment. We formalize agent reconnaissance by modeling the process and identifying the knowledge assets it seeks to extract: what they are, how they are used, and which agent weaknesses they exploit to give adversaries leverage in indirect prompt injection attacks. We instantiate these insights in Know Your Agent (KYA), a framework that automates black-box, reconnaissance-driven pentesting by probing agents, building target profiles, and using those profiles to craft stronger attacks. We evaluate KYA on agent-security benchmarks and a real-world coding agent, and release KYA, its benchmarks, and baseline implementations for reproducibility.

Abstract:Traditional pentesting uses reconnaissance at each step to uncover unseen weaknesses, build stronger attacks, and advance the objective; we argue that AI agents require the same treatment. We formalize agent reconnaissance by modeling the process and identifying the knowledge assets it seeks to extract: what they are, how they are used, and which agent weaknesses they exploit to give adversaries leverage in indirect prompt injection attacks. We instantiate these insights in Know Your Agent (KYA), a framework that automates black-box, reconnaissance-driven pentesting by probing agents, building target profiles, and using those profiles to craft stronger attacks. We evaluate KYA on agent-security benchmarks and a real-world coding agent, and release KYA, its benchmarks, and baseline implementations for reproducibility.

Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)

arXiv:2607.19837 [cs.AI]

(or arXiv:2607.19837v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.19837 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 218 words

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