SelfGrader: Stable Jailbreak Detection for Large Language Models using Token-Level Logits
SelfGrader: Stable Jailbreak Detection for Large Language Models using Token-Level Logits explores A novel, low-latency LLM guardrail that uses token-level logits to detect jailbreaks with significantly reduced resource overhead.. Commercial viability score: 7/10 in LLM Security.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $11K - $15K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisRecursive language models for jailbreak detection: a procedural defense for tool-augmented agentsViability: 3.0
- Builds On ThisStatistical Estimation of Adversarial Risk in Large Language Models under Best-of-N SamplingViability: 5.0
- Builds On ThisThe Struggle Between Continuation and Refusal: A Mechanistic Analysis of the Continuation-Triggered Jailbreak in LLMsViability: 3.0
- Builds On ThisIndicJR: A Judge-Free Benchmark of Jailbreak Robustness in South Asian LanguagesViability: 6.0
- Prior Work