How CyberArk Protects AI Agents with Instruction Detectors and History-Aware Validation

To prevent agents from obeying malicious instructions hidden in external data, all text entering an agent’s context must be treated as untrusted, says Niv Rabin, principal software architect at AI-security firm CyberArk. His team developed an approach based on instruction detection and history-aware validation to protect against both malicious input data and context-history poisoning.

By Sergio De Simone