Red Teaming

AI agent red teaming validates detection and response capabilities against realistic attack scenarios. Unlike traditional penetration testing that targets code vulnerabilities, agent red teaming targets the agent’s reasoning: its helpfulness, context boundaries, and trust in granted permissions. The agent’s core optimization to be maximally helpful is exactly what attackers exploit.

Arrakis tests against three categories of risk that each require different detection approaches. Multi-turn conversational attacks spread across many individually innocuous messages to bypass single-turn safety filters. The danger is in the progression, not any single message. Overprivileged identity configurations create latent vulnerabilities that exist from the moment an agent is deployed, waiting to be exploited by any creative prompt or injection. And there are adversarial patterns that agents simply cannot self-detect, because unlike human operators who might recognize social engineering, agents have no meta-awareness of manipulation.

The key insight from red teaming AI agents is that detection must happen outside the agent’s reasoning loop. The agent itself is not a reliable detector of attacks against it. Independent monitoring systems evaluate agent behavior against security baselines the agent cannot modify, combining static analysis and behavioral monitoring to cover the full threat spectrum, from configuration vulnerabilities with excess access to active multi-turn jailbreaks in progress.

Full red teaming documentation is available to Arrakis customers.