CyberIntel ⬡ News
★ Saved ◆ Cyber Reads
← Back ◬ AI & Machine Learning Aug 10, 2026

SynChain: Inducing Computer-Use Agent Systems to Construct Their Own Attack Chains

arXiv Security Archived Aug 10, 2026 ✓ Full text saved

arXiv:2608.06862v1 Announce Type: new Abstract: Computer-use agents~(CUAs) have transformed large language models into persistent execution systems capable of generating, storing, and reusing artifacts like skills and memory entries. However, existing security defenses largely treat attacks as externally triggered or temporally bounded, leaving a critical gap in addressing how compromise can propagate internally through an agent's own persistent state. We reveal that malicious influence can be c

Full text archived locally
✦ AI Summary · Claude Sonnet


    Computer Science > Cryptography and Security [Submitted on 7 Aug 2026] SynChain: Inducing Computer-Use Agent Systems to Construct Their Own Attack Chains Fuyao Zhang, Jiaming Zhang, Che Wang, Boyang Chen, Yurong Hao, Xiongtao Sun, Guowei Guan, Blaise Delattre, Yang Cao, Wei Yang Bryan Lim Computer-use agents~(CUAs) have transformed large language models into persistent execution systems capable of generating, storing, and reusing artifacts like skills and memory entries. However, existing security defenses largely treat attacks as externally triggered or temporally bounded, leaving a critical gap in addressing how compromise can propagate internally through an agent's own persistent state. We reveal that malicious influence can be covertly embedded into the structural redundancies of autonomously synthesized artifacts, allowing it to survive internal state updates and bypass standard vetting mechanisms. To formalize this threat, we introduce SynChain, a self-synthesized attack paradigm utilizing persistence-aware directed supervised fine-tuning to induce agents to create poisoned yet benign-looking artifacts. To systematically evaluate this propagation, we construct CUAChain, a dataset comprising 30 benign task chains and three attack objectives. SynChain enables dormant payloads to seamlessly reactivate in future workflows as trusted context, operating entirely without new malicious exogenous inputs. Extensive experiments on OpenClaw, Codex, and Claude Code under four defense settings demonstrate that SynChain achieves high attack success and outperforms adapted baselines, proving that securing CUAs requires provenance-aware reasoning over cross-task execution trajectories. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2608.06862 [cs.CR]   (or arXiv:2608.06862v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.06862 Focus to learn more Submission history From: Fuyao Zhang [view email] [v1] Fri, 7 Aug 2026 06:39:51 UTC (2,557 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs 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?)
    💬 Team Notes
    Article Info
    Source
    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Aug 10, 2026
    Archived
    Aug 10, 2026
    Full Text
    ✓ Saved locally
    Open Original ↗