Skip to content

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents

Jul 2026 · arXiv.org · Vol abs/2607.19837 · 0 citations · 36 references
Computer Science

TL;DR

This work formalizes agent reconnaissance by modeling the process and identifying the knowledge assets it seeks to extract, and instantiates KYA, a framework that automates black-box, reconnaissance-driven pentesting by probing agents, building target profiles, and using those profiles to craft stronger attacks.

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.

View source

Similar papers

#artificial intelligence Preprint Sep 2026

SoK: Rethinking Jailbreaking in the Era of Agentic AI: Attacks, Defenses, and Practical Consideration

A Systematization of Knowledge (SoK) that reframes jailbreak security around the full agentic execution pipeline, and develops unified taxonomies of attacks and defenses spanning user interaction, planning and reasoning, memory, tool use, and inter-agent communication, introduces a security--utility--efficiency evaluat...

Md. Jueal Mia, Yanzhao Wu, S. Uluagac et al. · 0 citations

AIOracles: Modeling Agentic AI Security *

A formal framework for security in agentic AI is developed, built around an ideal predicate ϕ that decides, for a given ( prompt, context, result ) triple, whether the agent’s behaviour is acceptable.

F. Durak, Tadayoshi Kohno, Franziska Roesner · 0 citations
Preprint Sep 2026

AgentLSD: Evaluating AI Security Agents Under Adversarial Task Contamination

Results show that clean CTF performance understates vulnerability to deceptive task evidence, and AgentLSD isolates this effect and provides a reproducible benchmark for studying it.

Matteo Golinelli, I. Drago, Matteo Boffa et al. · 0 citations
#artificial intelligence Preprint Sep 2026

AgentXploit: Autonomous Repository-to-Runtime Red-Teaming for AI Agents

This work presents AgentXploit, a two-role auditing system that separates repository-level attack-path discovery from runtime exploitation and introduces AgentXploit-Bench, containing 72 reproducible vulnerabilities across 12 open-source AI-agent systems and frameworks.

Wei-Da Liang, Shi Qiu, Zhun Wang et al. · 0 citations
Preprint Oct 2026

Red-Teaming Multi-Agent Attacks for Loss-of-Control Risks

This work demonstrates distributed attacks, in which different agents carry out separate attack fragments, and how their covertness evades frontier monitors when reading their trajectories individually, and provides a foundation for preventing locally benign agent actions from accumulating into system-level loss of con...

John Kitaoka, T. Pham, Carlos Giudice et al. · 0 citations
#artificial intelligence Preprint Oct 2026

AgentTrace: Execution-Level Fingerprinting of Black-Box LLM Agents

Verifying the provenance of autonomous LLM agents is a critical security challenge. Existing semantic attribution is easily obfuscated by system prompts, while active watermarking conflicts with schema-constrained tool generation. We propose AgentTrace, an active framework that identifies the foundation model behind a...

Ge-Jian Zhao, Han-Zhou Wu, Xin-Peng Zhang · 0 citations

Related blog posts

Microsoft Research Blog Nov 3, 2026

Learn the rules: The hacker mindset

Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.

MIT News · Artificial Intelligence Sep 29, 2026

Who we become when we talk to machines

Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.