AI/LLM Security Assessment
Understand whether an AI system exposes the organization to material security risk.
We help organizations identify whether AI applications expose sensitive data, grant excessive privileges, rely on ineffective guardrails, or create abuse paths that have not been independently validated under realistic offensive scenarios.
Risk made visible
Sensitive data exposure, insecure RAG, tool abuse, agent privilege, ineffective guardrails, missing monitoring, and weak incident readiness.
Offensive validation
Authorized attack simulation tests prompt injection, jailbreak attempts, indirect prompt injection, retrieval manipulation, tool abuse, and guardrail bypass scenarios.
Client receives
Validated findings, offensive test evidence, executive risk summary, framework mappings, remediation guidance, and retest plan.
Attack simulator capabilities
- Prompt injection and jailbreak scenario testing
- Indirect prompt injection through untrusted content
- Sensitive data exposure and retrieval abuse review
- RAG poisoning and source-trust validation scenarios
- Agent and tool abuse path testing
- MCP tool scope and authorization validation
- Guardrail bypass and policy violation attempts
- Logging, detection, and incident response validation
- Remediation roadmap and retest option
Responsible offensive testing
Attack simulation is performed only with written authorization, defined scope, and agreed rules of engagement. The goal is to prove which risks are exploitable, which controls work, and which weaknesses should be fixed first.
Need an independent AI security assessment?
Get a practical view of exploitable AI-specific risk and the controls most likely to reduce it.