Furina: Fragmented Uncertainty-Driven Refusal Instability Attack
The paper titled 'Furina: Fragmented Uncertainty-Driven Refusal Instability Attack' explores safety alignment in large language models. It challenges the assumption of deterministic safety behavior by revealing an instability region that leads to stochastic refusal decisions. The authors introduce a new attack method, Furina, which exploits this instability to enhance understanding of safety vulnerabilities.
- ▪The paper reveals that safety behavior in large language models is influenced by an instability region.
- ▪Furina is a jailbreak attack that uses fragmented prompts to induce uncertainty in model responses.
- ▪The research identifies a decoupling phenomenon where unstable inputs show high output uncertainty and low internal safety activation.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.26158 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
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| Cluster | 1zi3nrpY9iJQ |
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| Publisher visit | Yes — open original |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Cryptography and Security arXiv:2605.26158 (cs) [Submitted on 24 May 2026] Title:Furina: Fragmented Uncertainty-Driven Refusal Instability Attack Authors:Tongxi Wu, Jian Zhang, Yang Gao View a PDF of the paper titled Furina: Fragmented Uncertainty-Driven Refusal Instability Attack, by Tongxi Wu and 2 other authors View PDF HTML (experimental) Abstract:Safety alignment in large language models (LLMs) and multimodal large language models (MLLMs) is commonly assumed to operate as a near-binary threshold mechanism. We challenge this assumption by revealing that safety behavior is governed by an instability region where small perturbations induce stochastic refusal decisions rather than deterministic outcomes.
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