arXiv:2606.02959v1 Announce Type: new Abstract: 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. StratifiedKFold (by row) is the headline pass; a parallel StratifiedGroupKFold pass over a composite key (parent-prompt id plus MinHash + LSH near-duplicate clusters at Jaccard $\gt
The rapid deployment and increasing sophistication of Large Language Models necessitate robust security measures, as vulnerabilities like prompt injection become more prevalent and impactful.
Reliable benchmarking for LLM security is critical for developing trustworthy AI systems, protecting against misuse, and fostering broader adoption in sensitive applications.
The proposed methodology aims to standardize and improve the accuracy of LLM security evaluations, leading to more resilient AI and better informed development practices.
- · AI developers
- · Cybersecurity firms
- · Enterprise AI users
- · Malicious actors
- · Undetected LLM vulnerabilities
More secure Large Language Models become available for commercial and public use.
Increased trust in AI systems accelerates their integration into critical infrastructure and decision-making processes.
Standardized security benchmarks become a mandatory component of AI regulation and compliance frameworks worldwide.
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Read at arXiv cs.LG