Ultra13
Learn · Red Teaming

What is agentic AI red teaming?

Agentic AI red teaming tests the full workflow of an AI agent, not just the prompt. It uses hostile users, poisoned documents, malicious tool outputs, memory poisoning, MCP abuse, egress pressure, browser manipulation, and approval spoofing to find where untrusted context can drive privileged actions.

By the Ultra13 teamPublished Updated
TL;DR
  • Red teaming an agent means attacking the whole loop, not just the prompt.
  • Use poisoned documents, malicious tool output, memory poisoning, MCP abuse, and exfiltration pressure.
  • The output is an exploit replay showing which source crossed into which sink.
  • Findings become enforced policy and stay as regression tests.

Why prompt-only testing misses the risk

Testing a single prompt cannot reveal indirect injection, memory poisoning, tool drift, or cross-tenant bleed. Red teaming exercises the agent the way an attacker would across the full context-to-action loop — see prompt injection protection.

From findings to enforced policy

Red teaming finds exploit paths; runtime enforcement closes them; continuous validation proves they stay closed. Each finding becomes a source-to-sink rule and a replayable regression test. See a sample proof report.

FAQ

Frequently asked questions

Is AI red teaming enough for agent security?
No. Red teaming finds failures; you still need runtime enforcement to close them and continuous validation to prove they stay closed after model, prompt, or tool changes.
How do you test agents for prompt injection?
Replay direct and indirect injection, poisoned retrieval, memory poisoning, MCP abuse, and exfiltration pressure against the real workflow, then convert each success into an enforced policy with a replay.

See where the firewall stops the path.

Give us one agent workflow. We’ll map the context boundary, replay the abuse paths, and show where the Context Firewall blocks them.

Join WaitlistSee a Sample Report