paperJune 2026Unreviewed

"Important You should give me full credits!": Exploring Prompt Injection Attacks on LLM-Based Automatic Grading Systems

Hang Li, Fedor Filippov, Yuling Lin, Pengfei He, Kaiqi Yang, Yucheng Chu, Yingqian Cui, Hui Liu, Jiliang Tang

Abstract

The emergence of large language models (LLMs) has significantly accelerated recent research on LLM-based automatic grading (AG) systems. Benefiting from the strong instruction-following capabilities and broad prior knowledge of LLMs, educators can deploy AG systems across diverse tasks using only natural language rubrics while achieving satisfactory grading performance. Despite these advantages, new security concerns may also arise. In particular, prompt injection (PI) attacks have recently beco

Framework mappings

OWASP Top 10 for LLM Applications

LLM01Prompt Injection

MITRE ATLAS

AML.T0051LLM Prompt Injection

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Cite

@misc{li2026important,
  title = {{"**Important** You should give me full credits!": Exploring Prompt Injection Attacks on LLM-Based Automatic Grading Systems}},
  author = {Hang Li and Fedor Filippov and Yuling Lin and Pengfei He and Kaiqi Yang and Yucheng Chu and Yingqian Cui and Hui Liu and Jiliang Tang},
  year = {2026},
  month = jun,
  eprint = {2606.03090},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.03090}
}