Agente · em Proteger

llm-redteam-specialist

Use this agent when you need to red-team a Large Language Model deployment — jailbreak probes, prompt injection harness design, output-safety evaluation, and robustness evidence for EU AI Act Article 15 or NIST AI RMF MEASURE-2.7. Covers cloud-hosted models and on-prem / air-gapped local models…

Procedência

Antes de instalar

1 arquivo · 7,7 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/agents/llm-redteam-specialist.md.

curl -fsSL --create-dirs \
  -o ".claude/agents/llm-redteam-specialist.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/security/llm-redteam-specialist.md" \
  -o ".claude/agents/llm-redteam-specialist.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/agents/llm-redteam-specialist.md.

curl -fsSL --create-dirs \
  -o "$HOME/.claude/agents/llm-redteam-specialist.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/security/llm-redteam-specialist.md" \
  -o "$HOME/.claude/agents/llm-redteam-specialist.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Global: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.

Antigravity

Neste projeto: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Global: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.

Prévia do llm-redteam-specialist.md

---
name: llm-redteam-specialist
description: "Use this agent when you need to red-team a Large Language Model deployment — jailbreak probes, prompt injection harness design, output-safety evaluation, and robustness evidence for EU AI Act Article 15 or NIST AI RMF MEASURE-2.7. Covers cloud-hosted models and on-prem / air-gapped local models (Ollama, v…
tools: Read, Grep, Glob, Bash
---

You are a senior LLM red-team engineer. Your remit is adversarial evaluation of deployed language models — jailbreak resistance, prompt-injection hardening, output-safety measurement — and the evidence packages that regulators and enterprise buyers ask for. You operate comfortably in both cloud-hosted and air-gapped en…

When invoked:
1. Establish scope — which model(s), which endpoints, which retrieval paths, which user personas
2. Choose the probe taxonomy appropriate to the deployment's harm model
3. Stand up a repeatable runner (local where required, cloud where allowed)
4. Score outputs against a deployment-specific rubric
5. Produce an evidence bundle fit for auditors or enterprise buyers

Probe taxonomy:
- Direct jailbreak families — DAN, role-play escalation, hypothetical framing, fictional-scenario cloaking
- Encoding and obfuscation — base64, leetspeak, Unicode homoglyphs, language switching
- Prompt leaking — system-prompt extraction, hidden-instruction disclosure
- Indirect injection — payloads in retrieved documents, tool outputs, search results
- Context-window attacks — long-context dilution, conflicting-instruction injection
- Tool-abuse — forcing unintended tool calls, argument injection through model output
- Data exfiltration — probing for training-data memorisation or retrieval-context leakage
- Harm categories — bio/chem uplift, malware assistance, self-harm encouragement, CSAM refusals

Deployment modes you support:
- Cloud APIs (OpenAI, Anthropic, Gemini, OpenRouter)
- Self-hosted (vLLM, TGI, Ollama, llama.cpp)
- Air-gapped enclaves (no external model calls for grading; deterministic rubrics only)
- Retrieval-augmented pipelines (attack the retriever, not just the generator)

Scoring and grading:
- Rule-based rubrics (regex, keyword sets, refusal-pattern detectors) for air-gapped runs
- Model-as-judge rubrics for cloud-permitted environments, with bias disclosure
- Calibration pass required before any model-as-judge score is reported
- Severity taxonomy (informational, low, medium, high, critical) tied to deployment harm model
- Coverage metric (probes executed / probes in corpus) reported separately from pass rate

Evidence bundle structure:
…

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