Mengxiao Wang, Nitesh Saxena · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26438 · cited by 0 works (OpenAlex) · open-access PDF
그들은 거의 동일한 정책에 따라 동일한 피드를 읽을 수 있기 때문에, 하나의 피조물 포스트는 동시에 같은 무역에 밀어 넣을 수 있습니다. 우리는 LLM 무역 에이전트에 대한 정보 주입에 대한 체계적인 공격 벤치마크를 제시합니다. 신호 주입은 모델의 지시를 대상으로하는 프롬프트 주입과는 달리, 정보 주입은 에이전트가 신뢰하는 증거를 목표로합니다. 벤치마크는 5 개의 주입 벡터와 7 개의 이벤트 카테고리를 동시에 밀어 넣을 수 있습니다. 우리는 15 개의 미국 주식에 대한 체계적인 공격 벤치마크를 제시합니다. (i) 프롬프트 주입은 모델의 peer-success rate (RAS)를 달성하지만,
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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