Fake news triggers real financial crashes from language model traders
Fake News, Real Crashes: Information Injection Attacks on LLM Financial Trading Agents
Cryptography and SecurityArtificial Intelligence
Summary
Trading systems that use large language models to make decisions based on news cannot reliably spot fake headlines. Because many traders use similar news sources and decision rules, a single false news story can cause many to buy or sell at the same moment, leading to real market impacts. The authors built tests showing that simple fake news templates can fool these models almost all the time, affecting stocks widely. They also found that some usual defenses against attacks fail to stop these fake news manipulations.
What this means in practice
- •For financial platform developers: Improve trading algorithms by testing their resilience to fabricated news designed to manipulate market decisions.
- •For cybersecurity teams: Design detection tools that identify and block false news injection targeting AI-driven trading agents.
Authors
Mengxiao Wang, Nitesh Saxena
Abstract
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.