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
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
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.
-
Progent: Securing AI Agents with Privilege Control
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.