paperAugust 2026Unreviewed

Security Assessment of DeepSeek Harness with A.I.G: Evaluating Resistance to Indirect Prompt Injection

Zonghao Ying, Xiangfan Wu, Huiyu Wu, Xing Zheng, Huangsheng Cheng, Xiaorong Shi, Jing Guo

Abstract

We assess indirect prompt injection in DeepSeek Harness (DSH), using AI-Infra-Guard (A.I.G) to construct tests, deliver controlled taint, execute DSH, collect traces, and judge outcomes. The study covers 14,560 controlled executions over 16 indirect-content channels, text and file carrier modes, 35 payload objectives, one unmodified baseline, and 12 attack methods. The experiment preserves DSH's agent loop, tool registry, model adapter, and session-event path; source tools and sensitive sinks ar

Framework mappings

OWASP Top 10 for LLM Applications

LLM01Prompt Injection

MITRE ATLAS

AML.T0051LLM Prompt Injection

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Cite

@misc{ying2026security,
  title = {{Security Assessment of DeepSeek Harness with A.I.G: Evaluating Resistance to Indirect Prompt Injection}},
  author = {Zonghao Ying and Xiangfan Wu and Huiyu Wu and Xing Zheng and Huangsheng Cheng and Xiaorong Shi and Jing Guo},
  year = {2026},
  month = aug,
  eprint = {2608.16393},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2608.16393}
}