ai-threat-testing

To test LLM applications for OWASP Top 10 vulnerabilities like prompt injection, you need a structured penetration testing workflow that systematically exploits risks, captures execution artifacts as evidence, and generates actionable remediation guidance.

Assessing RAG security and vector database poisoning risks requires running targeted offensive tests against your RAG pipeline to identify data poisoning vulnerabilities, capture proof-of-concept evidence, and document findings with risk scoring for hardening.

Yes, AI security testing generates exploit evidence by capturing prompts, outputs, and logs during authorized model extraction attacks, allowing you to produce reproducible proof-of-concept findings for your security assessment.

To run a focused LLM vulnerability assessment for a production chatbot, apply specialized agent workflows to test specific OWASP LLM threats across full or focused scopes, generating prioritized vulnerabilities with PoCs and hardening guidance.

LLM penetration testing covers supply chain attacks and monitoring bypasses by executing specialized agent workflows designed to exploit these OWASP LLM Top 10 vulnerabilities and capture the resulting incident evidence.

AI security testing for LLM endpoints is limited to authorized penetration testing scopes, meaning it must only be applied to chat assistants, API endpoints, and RAG systems where explicit testing permission has been granted.