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A developer built a red-team test suite that sends adversarial prompts to an LLM-backed API to detect guardrail breaches. The suite separates the attack payload, the model provider, and the detector to isolate issues. The key challenge is that automated detectors often overcount attack success by flagging evasions that produce no real harm, requiring human reading of model replies to confirm actual harmful content. The approach includes iterative attack, hardening, and re-attack cycles on the same app, revealing subtle bypasses and detection gaps. Human review is used selectively on edge cases to improve accuracy and trust in automated verdicts.