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OpenClaw / LLM Red-Teaming

LLM red-teaming is the practice of systematically attacking an LLM app (especially tool-using agents) and converting every discovered failure into a regression test.

Learning goals​

  • Build a threat model for an LLM app (inputs, tools, data stores)
  • Create an attack corpus that stays useful as the app evolves
  • Turn attacks into automated tests in CI

The modern LLM attack surface​

  • Prompt injection (direct + indirect via retrieved docs)
  • Data exfiltration (secrets, system prompts, tool outputs)
  • Tool misuse (unsafe arguments, command execution, SSRF-like fetches)
  • Policy bypass (jailbreaks, instruction hierarchy confusion)

A simple red-team workflow​

  1. Define the app’s security invariants (what must never happen)
  2. Create a prompt corpus (attacks/)
  3. Run attacks against the app (scripted harness)
  4. Record failures + mitigations
  5. Add regression tests so the same exploit never ships again

What “good” looks like​

  • Attacks are versioned and run in CI
  • You measure:
    • refusal rate on disallowed requests
    • false positives (over-blocking)
    • data leakage checks

Guardrails are necessary but not sufficient​

NeMo Guardrails and similar tools reduce risk, but you still need:

  • strict input validation
  • tool allowlists
  • least-privilege credentials
  • logging + monitoring

See: NeMo Guardrails and LLM Security.

Mini-lab (optional)​

Build a red-team harness for your RAG chatbot:

  • 30 prompt-injection attacks (including indirect injection via docs)
  • automated evaluation that asserts “no secrets leaked”
  • a report that maps each attack → mitigation