Evaluating Prompt Injection Defenses for Educational LLM Tutors: Security-Usability-Latency Trade-offs
arXiv:2605.06669v2 Announce Type: replace-cross Abstract: Educational LLM tutors face a core AI alignment challenge: they must follow user intent while preserving pedagogical constraints and safety policies. We present an evaluation methodology for prompt-injection defenses in this setting, showing that guardrail design entails explicit trade-offs among adversarial robustness, benign-task usability, and response latency. We evaluate a domain-specific multi-layer safeguard pipeline combining deterministic pattern filters, structural validation, contextual sandboxing, and session-level behaviora
The proliferation of LLMs in sensitive applications like education necessitates robust defenses against adversarial attacks, making prompt injection a critical and immediate concern.
This research provides a framework for understanding the trade-offs in securing LLM-based tutors, a domain where maintaining pedagogical integrity and user safety is paramount.
The evaluation methodology and identified trade-offs will inform the development of more secure and context-aware LLM educational tools, leading to safer interactions and better learning outcomes.
- · AI guardrail developers
- · Educational technology companies
- · Students and educators
- · AI safety researchers
- · Malicious prompt engineers
- · Companies with insecure LLM products
Increased focus on robust AI safety mechanisms for domain-specific LLM applications.
Development of industry standards for prompt injection defense in educational and other sensitive AI systems.
Improved user trust and broader adoption of AI tutors due to enhanced security and reliability.
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Read at arXiv cs.LG