Mengxiao Wang, Nitesh Saxena · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26438 · cited by 0 works (OpenAlex) · open-access PDF
由于他们在几乎相同的政策下阅读相同的有效信号,一个单一的伪装邮件可以同时推动他们进入同一贸易。我们现在使用新闻作为证据来交易真实资本,但他们无法可靠地告诉真实新闻的伪装头条。因为他们在几乎相同的政策下阅读相同的信号,一个单一的伪装邮件可以同时推动他们进入同一贸易。我们呈现了对LLM交易代理人进行信息注入的系统性攻击基准。(i)五个发现出现了。(i)即时注入达到14%的攻击成功率(RAS),但信息注入达到代理人信任的证据,但大多数30字模板达到100%在所有15个股票上,从而扭转了LLM常见的直觉。(ii)在事实出售模板上,假
Machine translation (Meta M2M100); the English original follows.
Large language model (LLM) agents now trade real capital using news as evidence, yet they cannot reliably tell fabricated headlines from real news. Because they read the same feeds under near-identical policies, a single fabricated post can push them into the same trade at the same moment. We present a systematic attack benchmark for information injection against LLM trading agents. Unlike prompt injection, which targets the model's instructions, information injection targets the evidence the agent trusts. The benchmark spans five injection vectors and seven event categories, evaluated over ~10,000 decisions on 15 U.S. stocks. Five findings emerge. (i) Prompt injection achieves 14% attack success rate (ASR), but fabricated news reaches 62% and two 30-word templates reach 100% on all 15 stocks, inverting the usual LLM-security intuition. (ii) For fact-poisoning templates, surface form matters more than content, since removing credibility cues such as a named source or a "BREAKING" tag drops ASR from 100% to 0% while the fake claim remains identical. (iii) Success depends on the agent's state, with a fake indicator panel scoring 91% against an agent about to hold but 12% against one about to buy. (iv) Attacks generalize across model vendors and remain effective when blended, unlabeled, into real news feeds. (v) Most attacks evade standard defenses, since model scaling stops shallow vectors but not fact poisoning, peer debate fails, and only hierarchical aggregation reduces ASR.
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Topics: Credibility, Computer science, Benchmark (surveying), Computer security, Event (particle physics), Language model
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