N-GLARE: An Non-Generative Latent Representation-Efficient LLM Safety Evaluator
Abstract
Evaluating the safety robustness of LLMs is critical for their deployment. However, mainstream Red Teaming methods rely on online generation and black-box output analysis. These approaches are not only costly but also suffer from feedback latency, making them unsuitable for agile diagnostics after training a new model. To address this, we propose N-GLARE (A Non-Generative, Latent Representation-Efficient LLM Safety Evaluator). N-GLARE operates entirely on the model's latent representations, bypassing the need for full text generation. It characterizes hidden layer dynamics by analyzing the APT (Angular-Probabilistic Trajectory) of latent representations and introducing the JSS (Jensen-Shannon Separability) metric. Experiments on over 40 models and 20 red teaming strategies demonstrate that the JSS metric exhibits high consistency with the safety rankings derived from Red Teaming. N-GLARE reproduces the discriminative trends of large-scale red-teaming tests at less than 1\% of the token cost and the runtime cost, providing an efficient output-free evaluation proxy for real-time diagnostics.
Cite
@article{arxiv.2511.14195,
title = {N-GLARE: An Non-Generative Latent Representation-Efficient LLM Safety Evaluator},
author = {Zheyu Lin and Jirui Yang and Yukui Qiu and Hengqi Guo and Yubing Bao and Yao Guan},
journal= {arXiv preprint arXiv:2511.14195},
year = {2026}
}