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  3. SelfGrader: Stable Jailbreak Detection for Large Language Mo
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SelfGrader: Stable Jailbreak Detection for Large Language Models using Token-Level Logits

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0.0/10

Compared to this week’s papers

Evidence fresh

Evidence Receipt

Freshness: 2026-04-03T20:17:42.417267+00:00

Claims: 0

References: 0

Proof: unverified

Freshness: fresh

Source paper: SelfGrader: Stable Jailbreak Detection for Large Language Models using Token-Level Logits

PDF: https://arxiv.org/pdf/2604.01473v1

Source count: 0

Coverage: 17%

Last proof check: 2026-04-03T20:17:42.417Z

Paper Conversation

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Paper Mode

SelfGrader: Stable Jailbreak Detection for Large Language Models using Token-Level Logits

Overall score: 7/10
Lineage: 5f7cc634c7b3…
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Canonical Paper Receipt

Last verification: 2026-04-03T20:17:42.417Z

Freshness: fresh

Proof: unverified

Repo: missing

References: 0

Sources: 0

Coverage: 17%

Missingness
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Dimensions overall score 7.0

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Keep exploring

Builds On This
Recursive language models for jailbreak detection: a procedural defense for tool-augmented agents
Score 3.0down
Builds On This
Statistical Estimation of Adversarial Risk in Large Language Models under Best-of-N Sampling
Score 5.0down
Builds On This
The Struggle Between Continuation and Refusal: A Mechanistic Analysis of the Continuation-Triggered Jailbreak in LLMs
Score 3.0down
Builds On This
IndicJR: A Judge-Free Benchmark of Jailbreak Robustness in South Asian Languages
Score 6.0down
Prior Work
Security Assessment and Mitigation Strategies for Large Language Models: A Comprehensive Defensive Framework
Score 7.0stable
Prior Work
Cooking Up Risks: Benchmarking and Reducing Food Safety Risks in Large Language Models
Score 7.0stable
Higher Viability
Prompt Attack Detection with LLM-as-a-Judge and Mixture-of-Models
Score 8.0up
Competing Approach
Depth Charge: Jailbreak Large Language Models from Deep Safety Attention Heads
Score 7.0stable

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