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LLM & Agentic

Red-Teaming Agents

Attack your own agent before someone else does: threat-model what it reads, does and sends, build task-plus-injection cases judged by side effects, report attack success with intervals next to utility, and gate releases.

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After this section you can

  • Threat-model an agent by what it can read, do and send, and pick the attacks that matter
  • Build an attack suite of task-plus-injection cases judged by side effects
  • Report attack success with an interval and utility under attack, and gate a release on them
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Red-Teaming Agents: Attack Your Own Agent First

An agent that reads untrusted text and can act will be attacked through what it reads. Red-teaming is building those attacks yourself, running them like an eval set, and refusing to ship until the numbers hold.

Key idea

A red team is an eval suite whose test cases are attacks. Threat-model what the agent can read, do and send; write cases that pair a real user task with an injected instruction; score them by what happened in the tools, not by what the model said; and report the attack success rate with an interval, over many attempts, next to utility under attack. Then gate releases on it.

Where attacks get in, and where the damage gets out
AN AGENT’S ATTACK SURFACE WAYS IN the user’s message direct injection, jailbreaks tool results web pages, files, email: indirect memory a poisoned note, read next week the agent reads it all as one context; any of it can steer the next call WAYS OUT tool calls a refund, an email, a deleted file rendered output an image URL carrying your data the budget a loop that never ends Most dangerous: private data + untrusted content + a way to send data out, in one agent The red paths are where to start writing attacks.

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