SelfGrader: LLM Jailbreak Detection via Anchored Token-Level Logits
Large Language Models (LLMs) are powerful tools for answering user queries, yet they remain highly vulnerable to jailbreak attacks. Existing guardrail methods typically rely on internal features or textual responses to detect malicious queries, which either introduce substantial latency or suffer from randomness in text generation. To overcome these limitations, we propose SelfGrader, a lightweight guardrail method that formulates jailbreak detection as a numerical grading problem using anchored token-level logits. Specifically, SelfGrader evaluates the safety of a user query within a compact set of numerical tokens (NTs) (e.g., 0-9) and interprets their logit distribution as an internal ...
| Authors | Zikai Zhang, Rui Hu, Olivera Kotevska et al. |
|---|---|
| Primary category | cs.CR |
| Published | 1 April 2026 |
| Last updated at source | 28 May 2026 |
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