AI RESEARCH
Gate AI: LLM Security Benchmark Evaluation Methodology and Results
arXiv CS.LG
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ArXi:2606.02959v1 Announce Type: new Published evaluations of prompt-injection and jailbreak detectors for Large Language Models often suffer from two systematic weaknesses: per-dataset threshold tuning and undisclosed operating points. We describe an evaluation harness that addresses both. The detector under evaluation is scored across 16 public benchmarks (12,111 samples) using 5-fold cross-validation.