Exploring and Developing a Pre-Model Safeguard with Draft Models
Large Language Model (LLM) alignment remains vulnerable to jailbreak attacks that elicit unsafe responses, motivating pre-model and post-model guards. Pre-model guards audit the safety of prompts before invoking target models. However, relying solely on the prompt often leads to high false-negative rates (i.e., jailbreak attacks go undetected). Post-model guards address this issue by auditing both the user prompt and the target model's response. However, they incur a high computational cost, including increased token usage and processing time, because they operate after target model inference. In this paper, we introduce a safeguard design that leverages the transferability of jailbreak a...
| Authors | Hongyu Cai, Arjun Arunasalam, Yiming Liang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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