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Moving the Safety Barrier: Dynamic Routing Adaptive Alignment Against White-Box Attacks

arXiv Security Archived Aug 05, 2026 ✓ Full text saved

arXiv:2608.02674v1 Announce Type: new Abstract: With the widespread deployment of large foundation models (LFMs) in open environments, safety threats are shifting from black-box jailbreaks toward white-box attacks that directly identify and disrupt internal safety neurons or routes. However, existing safety defenses often rely on static safety units or fixed refusal pathways, leaving models highly vulnerable to targeted route-level white-box attacks. For that, we propose dynamic routing adaptive

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    Computer Science > Cryptography and Security [Submitted on 2 Aug 2026] Moving the Safety Barrier: Dynamic Routing Adaptive Alignment Against White-Box Attacks Shangze Li, Chuancheng Shi, Simiao Xie, Lingzhi He, Cheng Ji, Zifeng Cheng, Fei Shen, Chao Wu, Tat-Seng Chua With the widespread deployment of large foundation models (LFMs) in open environments, safety threats are shifting from black-box jailbreaks toward white-box attacks that directly identify and disrupt internal safety neurons or routes. However, existing safety defenses often rely on static safety units or fixed refusal pathways, leaving models highly vulnerable to targeted route-level white-box attacks. For that, we propose dynamic routing adaptive alignment (DRAA), a framework that introduces dynamic compensatory routes to preserve robust refusal behavior when the safety route is compromised. Specifically, we first identify and localize the model's safety route by contrasting internal activations between safe and unsafe calibration samples. DRAA then masks this safety route to induce causal failure cases and selectively mines the resulting defense failures, thereby constructing failure-aware preference pairs. Extensive experiments demonstrate that DRAA effectively restructures the underlying pathway dependence of model safety, substantially improving robustness against route-level white-box attacks, while preserving general utility. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.02674 [cs.CR]   (or arXiv:2608.02674v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.02674 Focus to learn more Submission history From: Chuancheng Shi [view email] [v1] Sun, 2 Aug 2026 17:45:20 UTC (1,508 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
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    ◬ AI & Machine Learning
    Published
    Aug 05, 2026
    Archived
    Aug 05, 2026
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