llamaguard-62d73cd0·2 events·first seen Aliases: LlamaGuard, LlamaGuard 3, LlamaGuard 4
A new arXiv preprint evaluates whether fine-tuned encoder classifiers from the ModernBERT family (ModernBERT and Ettin) can replace LLM-based safety judges for detecting harmful outputs in user-model conversations. The study benchmarks encoders against rule-based methods, fine-tuned LLM classifiers, and LLM judges including LlamaGuard 3/4, ShieldGemma, StrongReject, and Claude-as-a-judge across multiple adversarial attack types. Results are reported on F1, false negative rate, and precision-recall, with breakdowns by attack technique, providing practical guidance on cost-latency tradeoffs for production safety pipelines.
CyberSecEval 2 is a benchmark framework designed to evaluate both the cybersecurity risks and capabilities of large language models. The framework appears to be hosted or featured on Hugging Face's leaderboard infrastructure, extending prior cybersecurity evaluation work. It assesses LLMs across multiple dimensions of security-relevant behavior, including potential for misuse and defensive capabilities.