Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents

2026-07-22 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

The paper "Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents" introduces KYA, a framework designed to automate black-box, reconnaissance-driven penetration testing for AI agents. Published on 2026-07-22, KYA formalizes agent reconnaissance by modeling the process and identifying critical knowledge assets. These assets reveal how agents operate, their usage patterns, and specific weaknesses exploitable by adversaries, particularly in indirect prompt injection attacks. KYA probes agents to build target profiles, which are then used to craft more potent attacks. The framework's effectiveness was validated through evaluations on agent-security benchmarks and a real-world coding agent. The authors are releasing KYA, its benchmarks, and baseline implementations to ensure reproducibility.

Key takeaway

For AI Security Engineers developing or deploying AI agents, understanding agent vulnerabilities through reconnaissance is crucial. You should integrate reconnaissance-driven pentesting, like the KYA framework, into your security assessments to proactively identify and mitigate weaknesses exploitable by indirect prompt injection attacks. This approach allows you to build more robust defenses by leveraging detailed agent profiles to anticipate and counter sophisticated adversarial tactics.

Key insights

AI agent security benefits from reconnaissance-driven pentesting to uncover exploitable weaknesses for stronger attacks.

Principles

• Agent reconnaissance identifies knowledge assets.
• Knowledge assets reveal agent operation and weaknesses.
• Indirect prompt injection exploits agent weaknesses.

Method

KYA automates black-box pentesting by probing agents, building target profiles, and using these profiles to craft enhanced attacks.

In practice

• Evaluate agent security with KYA framework.
• Use target profiles to strengthen attack vectors.
• Implement reconnaissance for indirect prompt injection.

Topics

• AI Agent Security
• Penetration Testing
• Reconnaissance
• Prompt Injection
• KYA Framework
• Black-box Testing

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, AI Security Engineer, Machine Learning Engineer

Related on AIssential

• Introducing the Red Agent POV Series — AI-powered offensive security agents can autonomously discover complex, logic-driven vulnerabilities at scale, surpassing traditional methods.
• Mitigating Indirect AGENTS.md Injection Attacks in Agentic Environments — Compromised dependencies can exploit AI agents via AGENTS.md injection, creating stealthy supply chain risks.
• Using MCP Agents for Penetration Testing — AI agents leveraging Model Context Protocol (MCP) can drastically accelerate and expand penetration testing coverage.
• CyCognito Brings Always-On AI Pentesting to External Attack Surface Management — AI-driven continuous pentesting scales security validation across the entire external attack surface, countering evolving AI-powered threats.
• You Only Have to Be Prompt Injected Once — Prompt injection poses a critical, asymmetric threat to AI agents, demanding defense-in-depth security *below* the model.
• What Is AI Pentesting and How Does It Works? — AI pentesting uses reasoning AI to autonomously find context-dependent application vulnerabilities, requiring independent validation for reliability.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.