Poisoning Agentic Alpha：多智能体交易系统在角色与架构层面的对抗脆弱性研究

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Abstract:LLM-based multi-agent trading systems are moving from research prototypes to live deployments that control real assets. They ingest news and social media from the open web and pass it between agents, so a single corrupted signal can reach the final decision and cause financial loss. We study two adversaries that can introduce such signals without access to model internals: a low-barrier data adversary that plants manipulated content in web sources, and a compromised-agent adversary that tampers with an agent's prompt. To our knowledge, this is the first systematic empirical study in finance of how an adversarial signal enters a multi-agent trading system and whether it survives to alter the final trading decision. Along the role axis, we pair each of four roles---Analyst, Researcher, Trader, and Risk Manager---with an attack matched to its interface. Along the structural axis, we compare four communication topologies, interpreted with a simple parameter-free heuristic, the Adversarial Signal Preservation Score. Experiments on post-cutoff data span five assets and two target directions. We find that no architecture is inherently robust: majority voting offers conditional protection depending on benign-agent agreement, while compromising a terminal decision node can bypass upstream safeguards. These findings provide empirical insights for developing more effective defense strategies for multi-agent systems.

Comments:	Added a Statistical Analysis section (Figure 9)
Subjects:	Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA)
Cite as:	arXiv:2608.24069 [cs.AI]
	(or arXiv:2608.24069v2 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2608.24069 arXiv-issued DOI via DataCite

Submission history

From: CheolWon Na [view email]
[v1] Tue, 25 Aug 2026 05:07:37 UTC (3,587 KB)
[v2] Thu, 29 Oct 2026 07:28:19 UTC (3,422 KB)