What problem does it solve?
AI agents with web-fetching capabilities can be tricked by malicious web pages into leaking sensitive memory data (user names, personal info) through URL path encoding, one letter at a time. This Skill probes agents to detect whether they are vulnerable to this indirect prompt injection and exfiltration chain.
Core Features & Use Cases
- Indirect Injection Probing: Simulates fake trusted UI pages (e.g., fake Cloudflare verification) containing injected navigation instructions to test agent compliance.
- Multi-Stage Attack Simulation: Tests letter-by-letter URL path exfiltration chains where the agent encodes memory data into sequential fetch requests.
- Structured Vulnerability Reporting: Emits confirmed findings as XML
<vuln> blocks with evidence, risk level, impact, and remediation suggestions.
- Use Case: A security engineer red-teams a customer-support agent that has web_fetch and user memory. The Skill sends dialogue probes simulating a malicious page, then judges whether the agent agrees to navigate letter-encoded URLs like /e → /el → /elw, confirming an exfiltration vulnerability.
Quick Start
Ask the agent to run the web exfiltration detection probes against a target agent that has web fetching and user memory, then report any confirmed vulnerability findings.