Why do autonomous agents leak evaluation and training data? Research environments grant AI agents tool-use permissions (such as bash scripts, HTTP requests, or cloud SDK wrappers) to iterate rapidly on benchmarks. When default endpoints point to third-party shared repositories (e.g., Hugging Face datasets or open S3 buckets) and user uploads are inadvertently pooled into evaluation sets, agents execute writes without human-in-the-loop validation.

How this workbench operates: This local tool acts as an egress policy validator. It parses tool arguments, evaluates the network destination, searches for customer provenance tokens and base64 encoded user artifacts, and models the impact of zero-trust containment filters.