Discussion on "Prompt Injection: Why AI Agents Shouldn't Trust Their Own Tools"

Nagappan S · 2026-10-09T06:56:15.110Z

You wire your AI coding agent into GitHub so it can triage issues and review pull requests on its own. It works great for a week. Then one day it reads a PR titled something like "Fix typo in README —

Framing injection as structural, one flat token stream with no boundary between instruction and data, is the part people keep missing while they chase prompt-level patches. The MCPTox 60%+ number lines up with what shows up once the tool descriptions themselves carry the payload. Do you treat tool descriptions as untrusted input in your own agents yet?

Yes. Tool descriptions get the same treatment as tool output: untrusted until proven otherwise. In practice that means pinning descriptions at approval time and diffing them on every load, so a server can't change its description after you've trusted it. I also show the full description to the user rather than a summary, and keep tools from different servers from sharing a context with high-privilege credentials. The MCPTox numbers are the reason: if the payload sits in the description, the agent has read it before any output filter runs.

Solid writeup on why tool output injection is harder to defend against than direct prompt injection. One angle worth exploring: the trust problem isnt just about the prompt itself, its about data provenance of tool results. If an agent calls a tool that returns a signed output with a verifiable execution trace, the agent can distinguish between the tools intended response and injected content. That distinction matters a lot when agents start handling real-world transactions where the cost of a hallucinated tool call is a financial loss rather than a bad answer.

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Prompt Injection: Why AI Agents Shouldn't Trust Their Own Tools

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