llm-risk-assess

You can assess LLM application security risks by mapping data flows, threat modeling LLM providers, and running automated prompt injection probes to produce an OWASP-aligned risk matrix with remediation steps. The evaluation covers RAG components, tool integrations, and memory systems.

Testing for prompt injection and data poisoning in GenAI features involves executing automated security probes and vulnerability checks across your architecture. The process validates vector database trust, tests output handling, and scans for secrets to identify system vulnerabilities.

Yes, you can use automated red-team testing for LLM-enabled chatbots and agents. The assessment runs targeted prompt injection probes, validates plugin trust boundaries, and checks memory systems to document the attack surface and identify security risks.

To map threat models and trust boundaries for RAG architectures, you document LLM provider choices, data flows, and attack surfaces. This end-to-end architecture mapping identifies trust boundaries across vector databases, tool integrations, and memory systems.

Yes, the LLM security assessment generates compliance reports with remediation guidance. It produces risk matrices, evidenced findings, and remediation steps that cross-reference OWASP references for supply chain issues and data handling vulnerabilities.