ACCESSUS (2026)

A multi-agent simulation harness that places language-model agents inside an access-controlled organizational environment and logs every resource request, communication, policy verdict, and unauthorized data flow.

To prevent label contamination, access decisions and leak detection are adjudicated by a deterministic policy engine rather than an LLM judge. Each run separates ground-truth state (events.jsonl, labels.jsonl) from the partial view available to external monitors (observable.jsonl), supporting both insider-threat detection benchmarks and agent-compliance evaluations across model families.