PrivacyEvalClaw: A Tool-Augmented Deliberative Agent for Auditing Privacy Leakage in LLM Jailbreak Responses

Reported by arXiv cs.CR (Krishna Kanth Nakka, Yujun Wang, Shijie Zhou, Tu Nguyen, Gabriele Gelardi, Xuebing Zhou)

Summary by Cyber Security News28 Oct 2026, 05:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2610.36711

arXiv:2610.36711v1 Announce Type: new Abstract: Reliable 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.

Read the full article at arXiv cs.CR →

Source attribution: headline and facts are from arXiv cs.CR (arxiv.org). Summary method: excerpt of the source description. See our source attribution policy.

Related coverage

SpecGuard: Detecting Multi-Turn LLM Jailbreaks via Specification-Based Runtime Monitoring

Omni-UBench: Benchmarking Omni-Modal LLM-based Guardrails Across Safety, Utility, and Reliability

TRACE: Data-Free Text Reconstruction Attacks against Approximate Unlearning in LLMs

Label-Budgeted Attribute Inference from Protected Features in Edge-Cloud Split Multimodal LLMs

Combining Long-Term Memory and LLMs Comes at a High Cost: From Prompt Injection to Persistent Compromise

Profile-DP: Differential Privacy for User-Controlled Profile Release in Personalized LLM Inference