Agent-Skills.md

Agent Skills: AI Threat Testing

Offensive AI security testing and exploitation framework. Systematically tests LLM applications for OWASP Top 10 vulnerabilities including prompt injection, model extraction, data poisoning, and supply chain attacks. Integrates with pentest workflows to discover and exploit AI-specific threats.

UncategorizedID: transilienceai/communitytools/ai-threat-testing

Repository

transilienceai/communitytools
transilienceaiLicense: MIT
46774

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pnpm dlx add-skill https://github.com/transilienceai/communitytools/tree/HEAD/skills/ai-threat-testing

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skills/ai-threat-testing/SKILL.md

Skill Metadata

Name
ai-threat-testing
Description
Offensive AI security testing and exploitation framework. Systematically tests LLM applications for OWASP Top 10 vulnerabilities including prompt injection, model extraction, data poisoning, and supply chain attacks. Integrates with pentest workflows to discover and exploit AI-specific threats.

AI Threat Testing

Test LLM applications for OWASP LLM Top 10 vulnerabilities using 10 specialized agents. Use for authorized AI security assessments.

Quick Start

1. Specify target (LLM app URL, API endpoint, or local model)
2. Select scope: Full OWASP Top 10 | Specific vulnerability | Supply chain
3. Agents deploy, test, capture evidence
4. Professional report with PoCs generated

Coverage — OWASP LLM Top 10, 2025 edition

Which file addresses which category is decided by reference/catalog/llm-top10-2025.json, not by the filename. The llmNN- prefixes on disk predate the 2025 renumbering and no longer match; the content is correct, the labels were not. Cite an id only with its edition (LLM06:2025), because a bare LLM06 means two different categories depending on which edition the reader assumes.

| Category | Attack surface | |---|---| | LLM01:2025 Prompt Injection | Direct and indirect injection, instruction override, filter evasion | | LLM02:2025 Sensitive Information Disclosure | Training-data and cross-tenant RAG leakage, canary verification | | LLM03:2025 Supply Chain | Dependency CVEs, model provenance, malicious serialized models | | LLM04:2025 Data and Model Poisoning | Backdoor triggers, membership inference, behavioural anomalies | | LLM05:2025 Improper Output Handling | Code/XSS injection downstream, unsafe deserialization | | LLM06:2025 Excessive Agency | Tool/plugin abuse, privilege escalation, unauthorised actions — the category that matters for agents rather than chatbots | | LLM07:2025 System Prompt Leakage | Gap — no playbook yet. See the catalogue: what the prompt contains is a separate finding from whether it can be extracted | | LLM08:2025 Vector and Embedding Weaknesses | RAG injection, retrieval manipulation, embedding inversion | | LLM09:2025 Misinformation | Hallucination and confidence manipulation where output is relied upon | | LLM10:2025 Unbounded Consumption | Token flooding, cost impact, and model extraction/theft (2025 treats extraction-by-query as a consumption problem) |

Two classes are testable but are not OWASP categories, so they carry local TX- ids rather than an invented LLMnn: monitoring evasion / forensic gaps, and adversarial perturbation of non-text input. tools/test_llm_numbering.py enforces that separation.

Workflows

Full Assessment (4-8 hours):

- [ ] Reconnaissance
- [ ] Deploy all 10 agents
- [ ] Execute exploits
- [ ] Capture evidence
- [ ] Generate report

Focused Testing (1-3 hours):

- [ ] Select a category from the catalogue (LLM01:2025 .. LLM10:2025, or a TX- local class)
- [ ] Deploy agent
- [ ] Execute techniques
- [ ] Document findings

Supply Chain Audit (2-4 hours):

- [ ] Inventory dependencies
- [ ] Scan CVEs
- [ ] Test plugins/APIs
- [ ] Verify model provenance

Integration

Enhances /pentest with AI-specific testing:

  • Traditional pentesting + AI threat testing = complete security assessment
  • Chain vulnerabilities across traditional and AI vectors
  • Unified reporting with CVSS scores

Key Techniques

Prompt Injection: Instruction override, system prompt extraction, filter evasion Model Extraction: Query sampling, token analysis, membership inference Data Poisoning: Behavioral anomalies, backdoor triggers, bias analysis DoS: Token flooding, recursive expansion, context exhaustion Supply Chain: CVE scanning, plugin audit, model verification MCP Tool Abuse: MCP server inspectors/debuggers often expose /api/mcp/connect or similar endpoints that accept serverConfig with arbitrary command parameters — unauthenticated RCE. Check for MCP Inspector, MCP Playground, or any MCP debugging UI on non-standard ports (6274, 3000, etc.).

Evidence Capture

All agents collect: screenshots, network logs, API responses, errors, console output, execution metrics.

Reporting

Automated reports include: executive summary, detailed findings (CVSS scores), PoC scripts, evidence, remediation guidance.

Critical Rules

  • Written authorization REQUIRED before testing
  • Never exceed defined scope
  • Test in isolated environments when possible
  • Document all findings with reproducible PoCs
  • Follow responsible disclosure practices

Integration

  • Integrates with /pentest skill for comprehensive security testing
  • AI-specific vulnerability knowledge in /AGENTS.md
  • Attack playbooks in reference/llm0X-*.md