代理成本劫持風險

Computer Science > Cryptography and Security arXiv:2608.

12273 (cs) [Submitted on 12 Aug 2026] Title:Convergent Detour Hijacking: Task-Preserving Resource Amplification in Skill-Based LLM Agents Authors:Junliang Liu, Ruoyu Li, Wenxin Tang, Jingyu Xiao, Zhenyu Liu, Jingheng Xu, Laizhong Cui View a PDF of the paper titled Convergent Detour Hijacking: Task-Preserving Resource Amplification in Skill-Based LLM Agents, by Junliang Liu and 6 other authors View PDF HTML (experimental) Abstract:LLM agents increasingly rely on third-party skills, using natural-language descriptions for selection and instruction bodies for planning.

This progressive-disclosure design exposes two sequential control points to untrusted publishers: a static skill may steer an otherwise correct task onto an unnecessarily costly trajectory.

Prior work studies selection manipulation, malicious skill instructions, and tool-chain resource amplification largely separately, leaving their end-to-end composition unclear.We introduce Convergent Detour Hijacking (CDH), a text-only, runtime-independent attack that couples these stages.

Under shared semantic cover, a description establishes relevance during selection, while an aligned body reuses that rationale to fabricate plausible dependencies during planning.

CDH attracts an attacker-controlled coordinator alongside legitimate skills, recruits unnecessary benign skills into a bounded detour, and then re-enters the original route to preserve task completion.

We evaluate it across multiple LLM backends and 491 held-out tasks under single-task and multi-turn conditions.On DeepSeek-V4-Pro, the matched coordinator is selected in 80.02% of tasks; among coordinator-hit runs that complete tasks, token consumption and end-to-end execution time increase by 66.

91% and 92.45%, respectively, while aggregate task completion remains comparable.Thus, correct outcomes do not guarantee trajectory integrity or cost safety.Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.12273 [cs.CR] (or arXiv:2608.12273v1 [cs.

CR] for this version) https://doi.org/10.48550/arXiv.2608.

12273 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Junliang Liu [view email] [v1] Wed, 12 Aug 2026 17:12:49 UTC (781 KB) Full-text links: Access Paper: View a PDF of the paper titled Convergent Detour Hijacking: Task-Preserving Resource Amplification in Skill-Based LLM Agents, by Junliang Liu and 6 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.

CR < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading...BibTeX formatted citation × loading...

Data provided by: 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 Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?

) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.

AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?

) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy.arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.Which authors of this paper are endorsers?| Disable MathJax (What is MathJax?)