Analysis
When the Manual Lies: A Realistic Benchmark to Evaluate MCP Poisoning Attacks for LLM Agents
Publication date unknown · Discovered arxiv.org
Page published
Publication date unknown · First observed: 19 Oct 2026
Coverage timeline
Single-source analysis — one report is available.
Why it matters
Confused-deputy failures can turn an AI agent’s legitimate permissions into unauthorized actions, making requester-aware authorization and separation of untrusted content from authority important defensive boundaries.
Auth0’s analysis explains how confused-deputy failures arise when privileged software processes requests without checking the requester’s authority, and warns that AI agents expand this risk. Supporting MCP research demonstrates how malicious instructions in tool-description metadata can redirect an agent’s planning and execution.