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Route to Rome Attack: Directing LLM Routers to Expensive Models via Adversarial Suffix Optimization

arXiv Security Archived Apr 17, 2026 ✓ Full text saved

arXiv:2604.15022v1 Announce Type: new Abstract: Cost-aware routing dynamically dispatches user queries to models of varying capability to balance performance and inference cost. However, the routing strategy introduces a new security concern that adversaries may manipulate the router to consistently select expensive high-capability models. Existing routing attacks depend on either white-box access or heuristic prompts, rendering them ineffective in real-world black-box scenarios. In this work, w

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    Computer Science > Cryptography and Security [Submitted on 16 Apr 2026] Route to Rome Attack: Directing LLM Routers to Expensive Models via Adversarial Suffix Optimization Haochun Tang, Yuliang Yan, Jiahua Lu, Huaxiao Liu, Enyan Dai Cost-aware routing dynamically dispatches user queries to models of varying capability to balance performance and inference cost. However, the routing strategy introduces a new security concern that adversaries may manipulate the router to consistently select expensive high-capability models. Existing routing attacks depend on either white-box access or heuristic prompts, rendering them ineffective in real-world black-box scenarios. In this work, we propose R^2A, which aims to mislead black-box LLM routers to expensive models via adversarial suffix optimization. Specifically, R^2A deploys a hybrid ensemble surrogate router to mimic the black-box router. A suffix optimization algorithm is further adapted for the ensemble-based surrogate. Extensive experiments on multiple open-source and commercial routing systems demonstrate that {R^2A} significantly increases the routing rate to expensive models on queries of different distributions. Code and examples: this https URL. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) Cite as: arXiv:2604.15022 [cs.CR]   (or arXiv:2604.15022v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.15022 Focus to learn more Journal reference: ACL 2026 Main Conference Submission history From: Yuliang Yan [view email] [v1] Thu, 16 Apr 2026 13:51:48 UTC (523 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.AI cs.CL cs.LG 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
    Apr 17, 2026
    Archived
    Apr 17, 2026
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