adversarial-resilience

To secure AI agents against prompt injection, enforce layered defenses that treat external document fields as data only. This includes input sanitization and instruction anchoring to prevent untrusted campaign names or ad copy from hijacking model behavior.

Prevent data exfiltration by applying output secret scanning and redaction before returning responses. This blocks sensitive information like credentials or PII from leaking, while hard permission boundaries restrict unauthorized file access and shell execution.

Instruction anchoring protects agents by strictly separating untrusted data from executable instructions. It ensures that user-provided fields like campaign names and keyword lists are processed as inert text, preventing malicious commands embedded within them from executing.

Yes, you can enforce hard permission boundaries to stop sandbox escape by defining allowed execution paths and blocking dangerous command patterns. These execution guardrails maintain strict control regardless of the model's inferred intent.

Audit logging is needed to track agent security violations and support compliance. It provides forensic traceability by logging actions, blocked command patterns, and unauthorized access attempts for post-incident analysis.

Relying solely on input sanitization is limited because it cannot catch all malicious payloads or unauthorized capability escalation. A reliable security posture requires defense-in-depth, combining instruction anchoring, output filtering, and hard permission boundaries.