paperAugust 2026Unreviewed

ToolHazard: Scaling Adversarial Environments for Security Evaluation and Alignment of LLM-based Agents

Yutao Mou, Pengfei Yang, Zhe Yin, Zhangchi Xue, Xiaotian Luan, Dingyao Yu, Tong Zhang, Shikun Zhang, Wei Ye

Abstract

Large language model (LLM) agents integrated with external tools are vulnerable to indirect prompt injections embedded in environmental states. However, existing studies largely rely on manually implemented or reused environments, stochastic LLM-based tool simulation, and predefined injection locations, limiting scalable security research across broader domains. To bridge this gap, we propose **ToolHazard**, a scalable adversarial environment synthesis framework that reduces human engineering an

Framework mappings

OWASP Top 10 for LLM Applications

LLM01Prompt Injection

MITRE ATLAS

AML.T0051LLM Prompt Injection

Suggested from the entry's categories.

Cite

@misc{mou2026toolhazard,
  title = {{ToolHazard: Scaling Adversarial Environments for Security Evaluation and Alignment of LLM-based Agents}},
  author = {Yutao Mou and Pengfei Yang and Zhe Yin and Zhangchi Xue and Xiaotian Luan and Dingyao Yu and Tong Zhang and Shikun Zhang and Wei Ye},
  year = {2026},
  month = aug,
  eprint = {2608.11878},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.11878}
}