AI RESEARCH
GradingAttack: Exposing Security Vulnerabilities in LLM Based Educational Grading Agents
arXiv CS.AI
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ArXi:2602.00979v2 Announce Type: replace-cross Large language models (LLMs) are increasingly deployed as educational agents for automatic short answer grading (ASAG) in real-world educational environments, significantly boosting assessment efficiency and scalability. However, when these grading agents operate ``in the wild'', their vulnerability to adversarial manipulation raises critical concerns about agent security and trustworthiness. In this paper, we