Introduces a cross-validation-based evaluation methodology for LLM security detectors using a global threshold and group-fold leakage checks to avoid per-dataset tuning.
Gentel-safe: A uni- fied benchmark and shielding framework for defend- ing against prompt injection attacks
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Progent introduces a privilege-control framework for AI agents that uses LLM-generated symbolic rules over tools, SMT-solver-enforced monotonic updates, and deterministic checks to reduce attack success rates on AgentDojo and ASB benchmarks.
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Gate AI: LLM Security Benchmark Evaluation Methodology and Results
Introduces a cross-validation-based evaluation methodology for LLM security detectors using a global threshold and group-fold leakage checks to avoid per-dataset tuning.