AI Agent Goal Hijacking: Signals and Read-Only Goal Checks
Goal events concatenate the reported goal, plan, and decision.
These guides document actual 1-SEC implementation paths and source fixtures. Start by verifying the event producer and field meanings, then read the coverage limits before choosing an enforcement policy.
Goal events concatenate the reported goal, plan, and decision.
The built-in policy names blocked tools such as shell_exec, raw_sql, file_delete, and credential_access.
The memory monitor checks submitted writes for instruction-like content, a high write count within its window, and persistent/long_term memory associated with a changed session label.
The LLM Firewall examines reported decisions/plans for actions without required approval, selected action keywords under readonly/read_only/limited scopes, large tool counts, complex plans, and elevated cross-agent delegation.
Web-fetch tracking inspects reported URLs/domains for sensitive destinations, unusual fetch patterns, external-to-internal transitions, and some suspicious URL parameter content.
The action tracker keeps per-agent action counts and repeated action/target state.
Spawn handling computes a depth from reported agent/parent history, emits a new-agent observation, and increases severity when the computed depth exceeds three.
Failure tracking records errors within a five-minute window and counts retry-like error types.
The trust-exploitation handler raises an impersonation alert from an impersonation event.
The known-endpoint check compares reported destination hosts with its implemented AI-service host list.
The action tracker records target strings per agent.