评估教育LLM导师的提示注入防御：安全-可用性-延迟的权衡
Evaluating Prompt Injection Defenses for Educational LLM Tutors: Security-Usability-Latency Trade-offs
AI总结：
本文提出了一种评估提示注入防御方法的框架，探讨了在教育LLM导师中安全、可用性和延迟之间的权衡，并通过实验比较了不同防御机制的性能。
摘要：
教育LLM导师面临一个核心的AI对齐挑战：它们必须在遵循用户意图的同时保持教学约束和安全政策。我们提出了一个评估方法，用于评估提示注入防御在该场景中的表现，显示了防护栏设计在对抗性鲁棒性、良性任务可用性和响应延迟之间存在显式的权衡。我们评估了一个领域特定的多层安全防护流水线，结合确定性模式过滤器、结构验证、上下文沙箱和会话级行为检查。在受控的保留基准测试中，该流水线实现了低绕过率和假阳性率，同时优化了平均延迟——一个优先考虑教学可用性（零假阳性）而保持可测量攻击抵抗力的操作点。我们提供了一个可重复的基准测试协议，用于在相同条件下进行头对头比较，包括分层Bootstrap置信区间、配对McNemar显著性检验、多种子敏感度扫描，以及在相同划分上对Prompt Guard和NeMo Guardrails的直接评估。结果揭示了操作权衡：NeMo在16.22%的假阳性率下达到0%的绕过率，而Prompt Guard在3.60%的假阳性率下达到38.48%的绕过率。该框架支持在不同机构风险和可用性要求下，基于证据的防护栏选择。
英文摘要：
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 behavioral checks. On a controlled holdout benchmark, the pipeline reaches low bypass and false positive rates with optimized average latency - an operating point that prioritizes pedagogical usability (zero false positives) while maintaining measurable attack resistance. We provide a reproducible benchmark protocol for head-to-head comparison under identical conditions, including stratified bootstrap confidence intervals, paired McNemar significance tests, multi-seed sensitivity sweeps, and direct evaluation of Prompt Guard and NeMo Guardrails on the same split with unified instrumentation. Results expose operational trade-offs: NeMo reaches 0 percent bypass at 16.22 percent FPR and roughly 1.5s latency, while Prompt Guard yields 38.48 percent bypass with 3.60 percent FPR. The framework supports evidence-based guardrail selection for AI tutoring systems under different institutional risk and usability requirements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
