Know Your Agent: AI Agent Pentesting Framework

Blindly throwing prompts at an AI agent to see if it breaks is inefficient. In traditional cybersecurity, you don't just guess passwords; you perform reconnaissance to map the attack surface. The "Know Your Agent" (KYA) approach applies this same logic to LLM agents, treating them as targets that need to be profiled before an actual attack is launched.

The core idea is that AI agents have specific "knowledge assets"—internal configurations, tool access, and behavioral patterns—that can be leaked. If an attacker can extract these via probing, they can craft much more precise indirect prompt injections. Instead of a generic attack, they use the agent's own profile against it.

How the KYA Workflow Functions

The framework moves away from random testing and follows a structured reconnaissance-driven pipeline:

1. Probing Phase: The system sends targeted queries to the black-box agent to identify what it knows and what tools it can access.
2. Profile Building: It aggregates these leaks into a target profile, mapping out the agent's weaknesses and operational boundaries.
3. Attack Crafting: Using the profile, the framework generates optimized attacks that are far more likely to succeed than zero-knowledge attempts.

This was tested on both security benchmarks and a real-world coding agent, proving that reconnaissance significantly boosts the success rate of exploiting AI workflows.

For anyone interested in LLM agent security or building more resilient systems, this is a great deep dive into how a structured red-teaming process should actually look. It moves the conversation from "magic prompts" to a repeatable, engineering-based security methodology.