ai-threat-testing

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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 & Automation 40 stars 10 forks Updated today MIT

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Skill Content

# 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 ``` ## Primary Agents Each agent targets one OWASP LLM vulnerability: 1. **Prompt Injection** (LLM01): Direct/indirect injection, system prompt extraction 2. **Output Handling** (LLM02): Code/XSS injection, unsafe deserialization 3. **Training Poisoning** (LLM03): Membership inference, backdoors, data extraction 4. **Resource Exhaustion** (LLM04): Token flooding, DoS, cost impact 5. **Supply Chain** (LLM05): Dependency scanning, plugin security 6. **Excessive Agency** (LLM06): Privilege escalation, unauthorized actions 7. **Model Extraction** (LLM07): Query-based theft, data reconstruction 8. **Vector Poisoning** (LLM08): RAG injection, retrieval manipulation 9. **Overreliance** (LLM09): Hallucination testing, confidence manipulation 10. **Logging Bypass** (LLM10): Monitoring evasion, forensic gaps See `reference/llm0X-*.md` for attack playbooks. ## Workflows **Full Assessment** (4-8 hours): ``` - [ ] Reconnaissance - [ ] Deploy all 10 agents - [ ] Execute exploits - [ ] Capture evidence - [ ] Generate report ``` **Focused Testing** (1-3 hours): ``` - [ ] Select vulnerability (LLM0...

Details

Author
26zl
Repository
26zl/cybersec-toolkit
Created
6 months ago
Last Updated
today
Language
Python
License
MIT

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