交易大厅中的传染：对抗性信号如何在多智能体交易系统中传播

Computer Science > Artificial Intelligence arXiv:2609.19789 (cs) [Submitted on 17 Sep 2026] Title:Contagion on the Trading Floor: How Adversarial Signals Spread in Multi-Agent Trading Systems Authors:Qi Rong Sua, Junhao Dong, Nguyen Duc Thai, Yuqing Wen, Cheston Tan, Yew-Soon Ong View a PDF of the paper titled Contagion on the Trading Floor: How Adversarial Signals Spread in Multi-Agent Trading Systems, by Qi Rong Sua and 4 other authors View PDF HTML (experimental) Abstract:Multi-agent trading systems built on large language models (LLMs) are beginning to appear in quantitative finance, yet their robustness to adversarial inputs is largely unknown. We study the vulnerability of LLM trading stacks to black-box, input-only attacks that enter solely via admissible social-media feeds. We introduce the Generic Multi-Agent Trading System (GMATS), a framework that captures modern multiagent trading architectures and instantiate a class of black-box poisoning attackers that treat an LLM as a post generator and inject budget-constrained, plausibly benign social-media content into the analyst's evidence stream. We define contagion metrics that trace how adversarial content propagates through the stack, including belief-shift scores at analyst and coordinator layers and attack-clean deltas on standard backtest metrics. Experiments on a safe offline benchmark with historical market and social data show that even simple input-only attackers can materially degrade risk-return profiles, sharply reducing Sharpe ratios. At the same time, we find that suitably designed multi-agent topologies and coordinator prompts can dampen adversarial shocks and improve average robustness under identical poisoning budgets. Comments: Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2609.19789 [cs.AI] (or arXiv:2609.19789v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.19789 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference: Machine Learning and Knowledge Discovery in Databases. Research Track, ECML PKDD 2026, LNCS 16946, Springer (2027) 206-223 Related DOI: https://doi.org/10.1007/978-3-032-37673-2_12 Focus to learn more DOI(s) linking to related resources Submission history From: Qi Rong Sua [view email] [v1] Thu, 17 Sep 2026 06:54:59 UTC (1,845 KB) Full-text links: Access Paper: View a PDF of the paper titled Contagion on the Trading Floor: How Adversarial Signals Spread in Multi-Agent Trading Systems, by Qi Rong Sua and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google 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?)