@inproceedings{yang-etal-2026-steering,
title = "Steering {LLM} Viewpoints through Fabricated Evidence Injection",
author = "Yang, Xi and
Liu, Chang and
Huang, Zhenglin and
Li, Haoran and
Zhang, Weiming and
Weng, Jian and
Song, Yangqiu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.404/",
pages = "9310--9326",
ISBN = "979-8-89176-459-0",
abstract = "As chatbots increasingly influence daily decision-making, their potential to produce misleading responses poses substantial risks to users. This paper investigates a critical cognitive vulnerability in LLMs: their tendency to uncritically trust external context when presented with fabricated evidence bearing markers of credibility. We introduce Ghostwriter, a two-phase attack framework that first repackages misleading statements with fabricated rationales, then instruct target LLMs to incorporate these viewpoints when responding to relevant queries. Experiments on BBQ, ToxiGen, and our specialized dataset reveal that commercial LLMs without external safety classifiers remain highly vulnerable, while even frontier classifier-guarded models (e.g., GPT-5.4) reduce but do not eliminate the attack. Building on this, we explore multiple defense strategies, among which a tailored safety policy enables gpt-oss-safeguard to achieve 80.5{\%} detection rate."
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<abstract>As chatbots increasingly influence daily decision-making, their potential to produce misleading responses poses substantial risks to users. This paper investigates a critical cognitive vulnerability in LLMs: their tendency to uncritically trust external context when presented with fabricated evidence bearing markers of credibility. We introduce Ghostwriter, a two-phase attack framework that first repackages misleading statements with fabricated rationales, then instruct target LLMs to incorporate these viewpoints when responding to relevant queries. Experiments on BBQ, ToxiGen, and our specialized dataset reveal that commercial LLMs without external safety classifiers remain highly vulnerable, while even frontier classifier-guarded models (e.g., GPT-5.4) reduce but do not eliminate the attack. Building on this, we explore multiple defense strategies, among which a tailored safety policy enables gpt-oss-safeguard to achieve 80.5% detection rate.</abstract>
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%0 Conference Proceedings
%T Steering LLM Viewpoints through Fabricated Evidence Injection
%A Yang, Xi
%A Liu, Chang
%A Huang, Zhenglin
%A Li, Haoran
%A Zhang, Weiming
%A Weng, Jian
%A Song, Yangqiu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yang-etal-2026-steering
%X As chatbots increasingly influence daily decision-making, their potential to produce misleading responses poses substantial risks to users. This paper investigates a critical cognitive vulnerability in LLMs: their tendency to uncritically trust external context when presented with fabricated evidence bearing markers of credibility. We introduce Ghostwriter, a two-phase attack framework that first repackages misleading statements with fabricated rationales, then instruct target LLMs to incorporate these viewpoints when responding to relevant queries. Experiments on BBQ, ToxiGen, and our specialized dataset reveal that commercial LLMs without external safety classifiers remain highly vulnerable, while even frontier classifier-guarded models (e.g., GPT-5.4) reduce but do not eliminate the attack. Building on this, we explore multiple defense strategies, among which a tailored safety policy enables gpt-oss-safeguard to achieve 80.5% detection rate.
%U https://aclanthology.org/2026.emnlp-main.404/
%P 9310-9326
Markdown (Informal)
[Steering LLM Viewpoints through Fabricated Evidence Injection](https://aclanthology.org/2026.emnlp-main.404/) (Yang et al., EMNLP 2026)
ACL
- Xi Yang, Chang Liu, Zhenglin Huang, Haoran Li, Weiming Zhang, Jian Weng, and Yangqiu Song. 2026. Steering LLM Viewpoints through Fabricated Evidence Injection. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 9310–9326, Budapest, Hungary. Association for Computational Linguistics.