Validate the security controls protecting your AI

AI Security Validation is the practice of testing whether the controls protecting your AI applications, agents, and LLM deployments actually block the adversarial inputs attackers send. It connects to your AI system as a target, sends real prompt injections, jailbreaks, data-leakage attempts, and tool-abuse attacks at your deployed control, and reads back whether each one was blocked, refused, or reached the model.

AI red teaming and AI penetration tests are usually one-time, offensive engagements that probe a model's inherent behavior. AI Security Validation is a repeatable control-validation loop. It tests whether your deployed control catches a given attack and re-runs as new techniques appear, inside the same platform that validates your network, endpoint, and email controls, so the result is an ongoing measure of effectiveness rather than a point-in-time snapshot.

It ships 70+ attacks across five categories: prompt injection, jailbreaks, leakage, exploitation, and harmful content generation. That includes direct and indirect prompt injection, known and obfuscated jailbreaks, system-prompt and PII leakage, and critical-severity agent and MCP tool abuse such as privilege escalation, credential compromise, lateral movement, and remote code execution. You can also import your own attacks.

The controls around it. Picus validates the deployed guardrail, gateway, and policy enforcement protecting your AI, not the foundation model's training or alignment. The base model's inherent safety is the model vendor's responsibility. Your control is what stands between an adversarial input and the model, and that is what AI Security Validation proves.

Picus connects to internal AI applications, agents, and LLM deployments, as well as the commercial guardrails and gateways in front of them, using an agentless, API-based approach. It handles both stateless endpoints and session-based flows, and supports API key, Bearer, and Basic authentication so it can reach protected endpoints securely.

Yes. Picus runs adversarial inputs against the controls around your specific AI application, agent, or LLM and reads back how each input was handled, so the result reflects your deployment and your configuration rather than a generic model. Attacks run context-aware, adapting to the target's stated purpose and boundaries.

Whenever something changes that could affect the answer: a new jailbreak or injection technique, a model or guardrail update, or a policy change. Because new techniques surface constantly, Picus runs validation as a signal-driven loop rather than on a fixed calendar, so a control that passed last month is re-validated against this week's attacks.

Every result maps automatically to MITRE ATLAS, OWASP LLM Top 10, and NIST AI RMF, and the platform ships standards-aligned playbooks for each. You get a live coverage view per framework and exportable, audit-ready evidence you can take to leadership.

Picus runs adversarial inputs against a designated, scoped AI target or controlled endpoint you define, reads back how the control handled each input, and reports a verdict without persisting injected content.