Dive into Ambiguity: A*-Inspired Multi-Agents Commonsense Obfuscation Attack on LLM Prompts

Abstract

Large language models (LLMs) excel in reasoning and knowledge-intensive tasks but remain vulnerable to prompt-level adversarial attacks that preserve intent while triggering commonsense hallucinations. This vulnerability is urgent, as LLMs are rapidly integrated into safety-critical domains where factual reliability is non-negotiable. Existing attack methods either lack efficiency or fail to capture the adaptive strategies of real-world adversaries. We propose an A*-inspired Factual Error Induct

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@misc{wang2026dive,
  title = {{Dive into Ambiguity: A*-Inspired Multi-Agents Commonsense Obfuscation Attack on LLM Prompts}},
  author = {Boxuan Wang and Zhuoyun Li and Xiaowei Huang and Yi Dong},
  year = {2026},
  month = may,
  eprint = {2606.01441},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.01441}
}