Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 36 references that are in the map. First posted on arXiv 22 Jul 2026.
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23 of 36 references.
References · 23
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
- 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
- 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,173
- 2024OpenHands: An Open Platform for AI Software Developers as Generalist Agentscited by 956
- 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 760
- 2023Prompt Injection attack against LLM-integrated Applicationscited by 568
- 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agentscited by 476
- 2024Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agentscited by 341
- 2025Prompt Injection Attack to Tool Selection in LLM Agentscited by 145
- 2022"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practicecited by 55
11 more
- 2025AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agentscited by 42
- 2025ChatInject: Abusing Chat Templates for Prompt Injection in LLM Agentscited by 28
- 2025Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competitioncited by 22
- 2025ToolTweak: An Attack on Tool Selection in LLM-based Agentscited by 22
- 2025UDora: A Unified Red Teaming Framework against LLM Agents by Dynamically Hijacking Their Own Reasoningcited by 21
- 2025ObliInjection: Order-Oblivious Prompt Injection Attack to LLM Agents with Multi-source Datacited by 17
- 2026AdapTools: Adaptive Tool-based Indirect Prompt Injection Attacks on Agentic LLMscited by 12
- 2025TopicAttack: An Indirect Prompt Injection Attack via Topic Transitioncited by 11
- 2026Trojan's Whisper: Stealthy Manipulation of OpenClaw through Injected Bootstrapped Guidancecited by 8
- 2024Universal and Context-Independent Triggers for Precise Control of LLM Outputscited by 3
- 2026VeriGrey: Greybox Agent Validationcited by 1
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- 2026From Controlled to the Wild: Evaluation of Pentesting Agents for the Real-World
- 2026Architecting Secure AI Agents: Perspectives on System-Level Defenses Against Indirect Prompt Injection Attacks
- 2026Toward Secure AI-Powered Penetration Testing Agents: Security Threats, Guardrails, and Architectural Perspectives
- 2026Rethinking Indirect Prompt Injection as a Test-Time Search Problem
- 2024AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
- 2026SkillAtlas: An Attack Trace Library for Agent Skills
- 2025PentestJudge: Judging Agent Behavior Against Operational Requirements
- 2026PenForge: On-the-Fly Expert Agent Construction for Automated Penetration Testing