arXiv Computation and Language
Oct 1

Safety Monitors Mostly Catch What the Model Already Refuses

The paper evaluates safety monitors by measuring recall only on prompts that the target model actually answers, rather than on all harmful prompts. Across several guard systems, recall at a 1% false‑positive rate drops sharply when focusing on answered prompts, with monitors catching refused requests 1.1–6.4 times more often than answered ones. Rewriting prompts to be less explicit dramatically increases compliance and reveals that many harmful requests slip past monitors, especially when phrasing is softened. Fine‑tuning guards on these rewritten prompts improves recall from 0.24 to 0.89 on answered requests and generalizes to unseen benchmarks.

By Sripad Karne
arXiv AI
Sep 25

Refusing Everything Looks Safe: Restoring the Benign Arm to Encoded-Prompt Evaluation

The paper critiques current encoded‑prompt safety benchmarks that focus only on harmful requests, showing that such tests can misrepresent a model’s safety. By evaluating the benign arm under the same encoding, the authors reveal a substantial drop in the harm gap—sometimes to zero—indicating that the encoding masks true safety deficiencies. Across multiple large models and training pipelines, they document that the encoding can either hide or falsely inflate safety metrics, and they identify twelve specific instrument defects that contribute to these misleading results.

By Haoyu Zhang, Haowen Xu, Xiao Luo, Hanwen Liu, Yang Chen, Zijian Xiao, Yi Feng, Xiangchen Guan, Mohammad Zandsalimy, Shanu Sushmita
arXiv AI
Oct 15

Same Attack, Different Judge: A First Controlled Adversarial Comparison of Jev, LLM Judges, and Purpose-Built Safety Classifiers

The paper evaluates TypeSafe AI’s Jev safety judge against GPT and GLM judges and local classifiers using a controlled adversarial test harness. Jev shows strong robustness on rule‑based tasks like input moderation, with minimal bypasses, but performs less well on judgment‑based tasks such as phishing detection, where it misses a higher proportion of phishing emails and is more prone to false positives. The study highlights Jev’s speed advantage and varying resilience depending on task type.

By Xiang Li, Aichun Huang, Mingming Zhang, Zheli Liu