Developer Publishes Reusable Test Harness to Probe AI Agent Tool Boundaries

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A software developer has shared a repeatable Python-based test harness designed to expose security weaknesses in AI agents before they reach production. The tool targets four common failure modes: scope creep, confused deputy attacks, escalation by composition, and silent refusal failures, where an agent claims to decline a request but executes it anyway. A key design principle of the harness is that it evaluates only the structured tool-call trace rather than the agent's natural-language output, which the author argues is inherently untrustworthy. The harness uses a combination of hand-written seed cases and an LLM-generated adversarial input set to stress-test tool boundary rules defined in a schema. It is built to run in CI pipelines and is compatible with free-tier models to avoid unexpected costs during red-team testing.

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