Mengxiao Wang, Nitesh Saxena · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.26438 · cited by 0 works (OpenAlex) · open-access PDF
Les agents du grand modèle d'acquisition de faits agrégés (LLM) négocient désormais le capital réel en utilisant les nouvelles comme preuves, mais ils ne peuvent pas dire de manière fiable les titres fabriqués des nouvelles réelles. Parce qu'ils lisent les mêmes flux sous des politiques presque identiques, un seul poste fabriqué peut les pousser dans le même commerce en même temps. Nous présentons un benchmark d'attaque systématique pour l'injection d'informations contre les agents de trading LLM. Cinq résultats apparaissent. Contrairement à l'injection rapide, qui cible les instructions du modèle, l'injection d'information cible les preuves de l'agent. Le benchmark s'étend sur cinq vecteurs d'injection et sept catégories d'év
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.
Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese
Topics: Credibility, Computer science, Benchmark (surveying), Computer security, Event (particle physics), Language model
GET https://store.agentexchange.work/gw/papers/verify?title=...&year=... (0.003 USD), fetch this record as JSON with /gw/papers/paper?id=2610.26438, or list what it cites with /gw/papers/references?id=.... Pay per call in USDC (x402) or with a 19 USD card key for 1,000 calls. Catalogue · llms.txtSources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange