New LeakGauge Method Detects Sensitive Data Disclosure in LLMs

Researchers have developed a new method called LeakGauge to detect when large language models might reveal sensitive external contexts. This technique analyzes the prefill token probabilities of a model's response to generate an attack-risk score, indicating potential leakage. LeakGauge has demonstrated high accuracy across various LLMs, including GLM-5.2 and Kimi-K3, and can be implemented with minimal additional parameters and latency. AI

IMPACT Enhances LLM security by providing a tool to detect and mitigate sensitive data leakage.

RANK_REASON The cluster contains a research paper detailing a new method for LLM security. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• GLM-5.2
• Gotit.pub
• Hugging Face
• Kimi-K3
• LeakGauge
• ScienceCast

• paper
• safety

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