{
  "id": 14482,
  "url": "https://arxiv.org/abs/2607.25227v1",
  "title": "Decision-Level Hijacking: Injecting Cognitive Bias into Large Language Models via Bit-Flip Attacks",
  "summary": "Large Language Models (LLMs) have been widely applied in high-stakes decision-making scenarios such as corporate strategy, and users are increasingly relying on their outputs. However, the deep integration of open-source model sharing ecosystems with LLM-powered critical decision-making applications also introduces critical risks: if an attacker can manipulate the model's cognitive stance, they can indirectly influence the judgments and actions of downstream decision-makers. This paper defines s",
  "authors": "Yu Yan, Jiahao Chen, Siqi Lu, Yongjuan Wang, Ziming Zhao, Zhaoxuan Li et al.",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-28T02:59:32.000Z",
  "fetched_at": "2026-07-29T05:10:12.205Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14482",
  "original_url": "https://arxiv.org/abs/2607.25227v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}