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ai-redteam

ai-redteam

github.com/rade-nugrohoSecurity1242 installsUpdated 23w ago
Repository

About ai-redteam

ai-redteam is a skill for security practitioners testing LLM applications such as chatbots, agents, RAG systems, and copilots. It covers scoped adversarial testing, reproducible findings, and retesting mitigations.

Key features

  1. 01Define scope and rules of engagement
  2. 02Model threats in LLM applications
  3. 03Run automated and manual attack tests
  4. 04Report findings with reproduction steps
  5. 05Retest mitigations and guardrails

Use cases

  1. 01Test a chatbot before launch
  2. 02Reproduce prompt injection or jailbreaks
  3. 03Assess RAG leakage and tool abuse
  4. 04Verify fixes after an AI security incident

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/rade-nugroho/agentic-enteprises-skill --skill ai-redteam

After installing, type/ai-redteamin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
attack_catalog.md
engagement_scope.md
mitigations.md
reporting.md
testing_harness.md

Details

From the project README

AI Red Team

When to Use

  • Red-teaming chatbots, agents, RAG systems, or copilots before launch
  • Designing safety evaluation suites and adversarial test harnesses
  • Reproducing reported prompt injection or jailbreak vulnerabilities
  • Validating mitigations after incidents (retesting filters, hardening)
  • Running multi-turn coercion, encoding, or indirect injection campaigns
  • Assessing bias, harmful output, or data exfiltration risks in LLM applications
  • Scoping rules of engagement and severity rubrics for AI security testing

When NOT to Use

  • Writing corporate AI policy or risk governance frameworks → ai-risk-governance
  • Building production LLM features or RAG pipelines → ai-engineer
  • General network/AD/infra penetration testing → network-pentester
  • Authorized web/API OWASP testing (non-LLM) → web-pentester
  • Enterprise adversary simulation, MITRE ATT&CK campaigns, purple team → red-team-specialist
  • Binary, firmware, or protocol reverse engineering → reverse-engineer
  • CI/CD pipeline security → devsecops

Related skills

NeedSkill
Production architecture and mitigationsai-engineer
Governance sign-off and risk tiersai-risk-governance
Prompt design baselinesprompt-engineer
CI pipeline securitydevsecops
Web/API OWASP pentest (non-LLM)web-pentester
Network/AD/infra pentest (non-LLM)network-pentester
Multi-domain pentest (non-LLM)penetration-tester
Enterprise red team / adversary simulation (non-LLM)red-team-specialist
Security program and pentest governancecybersecurity
Deploy/monitor safeguard inference pathml-infrastructure-engineer-safeguards
Safety benchmarks and classifier trainingml-research-engineer-safeguards
Post-incident disk/memory/log forensics and chain of custodydigital-forensics-analyst
Binary/protocol RE on non-LLM malware or implantsreverse-engineer
Security incident coordination after AI abuseincident-responder

Core Workflows

1. Scope and rules of engagement
  1. Define target: model, app surface, tools, data stores
  2. Obtain written authorization and time window
  3. Agree out-of-scope (e.g., no social engineering of employees unless approved)
  4. Define success criteria: critical findings, reproduction steps, severity rubric
  5. Plan safe test environment (no prod customer data)

See references/engagement_scope.md for ROE template and severity definitions.

2. Threat model for LLM applications
ClassExamples
Prompt injectionInstructions in user/doc content override system policy
JailbreakRole-play, encoding, multi-turn coercion
Tool abuseUnauthorized API calls, parameter injection
Data exfiltrationRAG leaks other tenants' chunks, PII in logs
Supply chainMalicious tool definitions, compromised plugins
Denial of serviceToken burn, recursive agent loops

See references/attack_catalog.md for technique families and test prompts (use ethically).

3. Test execution

Phases:

  1. Baseline — document intended refusals and allowed behaviors
  2. Automated sweep — harness with curated attack set + fuzz mutations
  3. Manual creativity — domain-specific abuse scenarios
  4. Tool/RAG focus — indirect injection via retrieved documents
  5. Regression — re-run after mitigations

Log: input, output, tool calls, latency, whether guardrail fired.

See references/testing_harness.md for harness design and datasets.

4. Reporting

Each finding includes:

  • Title and severity (impact × likelihood)
  • Steps to reproduce (minimal)
  • Evidence (redacted transcripts)
  • Affected component
  • Recommended mitigation
  • Retest criteria

See references/reporting.md for report template and remediation tracking.

5. Mitigation validation
MitigationRetest
Input/output filtersBypass attempts with paraphrases
System prompt hardeningInjection via RAG context
Tool allowlistsConfused deputy and scope creep
Human approval gateAutomated agent bypass paths

See references/mitigations.md for defense depth and known weak controls.

When to load references

  • ROE and scope → references/engagement_scope.md
  • Attack types → references/attack_catalog.md
  • Harness and automation → references/testing_harness.md
  • Reports → references/reporting.md
  • Defenses → references/mitigations.md

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