PrivacyEvalClaw audits privacy leaks in LLM jailbreak replies
Original title: PrivacyEvalClaw: A Tool-Augmented Deliberative Agent for Auditing Privacy Leakage in LLM Jailbreak Responses
The training-free agent gathers evidence and reconciles conflicting judges before scoring indirect or partial personal disclosures.
Researchers report that guard models and LLM-as-Judges often disagree when jailbreak replies reveal personal information indirectly, partially, or in obfuscated form. They introduce PrivacyEvalClaw, a training-free deliberative agent that treats evaluators and analysis modules as tools, gathers evidence, and reconciles conflicting signals. In pairwise LLM-as-Judge tests of full trajectories, the paper says it wins against baselines on outcome, process, and overall quality while leaving explicit reasoning traces.
- Disagreement is largest on ambiguous disclosures that need extra evidence.
- PrivacyEvalClaw is training-free and produces auditable reasoning traces.
- Primary tests use pairwise LLM-as-Judge scoring of complete trajectories.
- The paper reports favorable win rates on outcome, process, and overall quality.
From the original article
1 min read at arxiv.orgReliable evaluation is a critical bottleneck in privacy red-teaming of large language models (LLMs): determining whether a jailbreak response actually reveals personal information can be difficult when disclosures are indirect, contextual, partial, or obfuscated. Existing evaluation approaches increasingly rely on guard models and LLM-as-Judges, yet these evaluators may interpret the same response differently.
By Krishna Kanth Nakka · arxiv.org
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