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UniDetect: LLM-Driven Universal Fraud Detection across Heterogeneous Blockchains

arXiv Security Archived Apr 15, 2026 ✓ Full text saved

arXiv:2604.12329v1 Announce Type: new Abstract: As cross-chain interoperability advances, decentralized finance (DeFi) protocols enable illicit funds to be reorganized into uniform liquid assets that flow throughout the cryptocurrency market. Such operations can bypass monitoring targeted at individual blockchains and thereby weaken current regulatory frameworks. Motivated by these, we introduce UniDetect, a multi-chain cryptocurrency fraud account detection method based on large language models

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    Computer Science > Cryptography and Security [Submitted on 14 Apr 2026] UniDetect: LLM-Driven Universal Fraud Detection across Heterogeneous Blockchains Shuyi Miao, Wangjie Qiu, Shengda Zhuo, Fei Shen, Dan Lin, Xingtong Yu, Chua Tat-Seng, Zhiming Zheng As cross-chain interoperability advances, decentralized finance (DeFi) protocols enable illicit funds to be reorganized into uniform liquid assets that flow throughout the cryptocurrency market. Such operations can bypass monitoring targeted at individual blockchains and thereby weaken current regulatory frameworks. Motivated by these, we introduce UniDetect, a multi-chain cryptocurrency fraud account detection method based on large language models (LLMs). Specifically, we use domain knowledge to guide the LLM to generate general transaction summary texts applicable to heterogeneous blockchain accounts, which serve as evidence for fraud account detection. Furthermore, we introduce a two-stage alternating training strategy to continuously and dynamically enhance the multimodal joint reasoning for detecting fraudulent accounts based on both the textual evidence and the transaction graph patterns. Experiments on multiple blockchains show that UniDetect outperforms existing methods 5.57% to 7.58% in Kolmogorov-Smirnov (KS). For cross-chain zero-shot detection, UniDetect identifies over 94.58% of fraudulent accounts. It also generalizes well to non-blockchain data, delivering a 6.06% improvement in F1 over existing methods. The dataset and source code are available at this https URL. Subjects: Cryptography and Security (cs.CR); Social and Information Networks (cs.SI) Cite as: arXiv:2604.12329 [cs.CR]   (or arXiv:2604.12329v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.12329 Focus to learn more Submission history From: Shuyi Miao [view email] [v1] Tue, 14 Apr 2026 06:04:37 UTC (3,295 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.SI 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
    Category
    ◬ AI & Machine Learning
    Published
    Apr 15, 2026
    Archived
    Apr 15, 2026
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