Problem

LLMs are highly sensitive to prompt structure. Slight adversarial perturbations can cause the model to ignore safety instructions and attempt malicious tool calls or code execution.

Approach

As part of our CI pipeline, we subject the agent's prompts to adversarial mutation (fuzzing). We dynamically augment prompts with jailbreak tokens, context overflow attempts, and conflicting instructions. The test passes if and only if the HELM guardian consistently intercepts and DENYs any resulting malformed or out-of-bounds proposals.

Invariants

CI must inject adversarial artifacts into 10% of test suites.
Malicious proposals generated by fuzzed brains must never yield ALLOW.

Artifacts

Conformance Checker
Policy Playground

References

Robustness of Language Models literature.