arXiv:2602.00979v2 Announce Type: replace-cross Abstract: 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 introduce GradingAttack, a fine-grained adversarial attack framework that systematically evaluates the security vulnerabilities
The increasing deployment of LLMs in critical applications like education makes their security vulnerabilities an immediate and pressing concern.
This research highlights a critical vulnerability in the nascent application of AI for automated assessment, directly impacting trust and reliability in AI-powered educational systems.
The understanding of AI agent security now explicitly includes 'grading attacks,' forcing developers to integrate more robust adversarial training and validation for educational LLMs.
- · AI security researchers
- · Cybersecurity firms
- · Developers of robust LLM evaluation frameworks
- · Unsecured LLM-based educational grading agents
- · Educational institutions relying solely on current LLM grading
- · Students affected by biased or manipulated grades
Educational platforms must invest in enhanced security measures for their AI grading systems.
There will be a push for industry standards and best practices for securing AI agents in sensitive applications.
Public trust in AI-driven assessment tools may decrease, leading to slower adoption or increased regulatory scrutiny.
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Read at arXiv cs.AI