招聘中的木马：针对标准与推理模型间接提示注入的红队案例研究
Trojan Horses in Recruiting: A Red-Teaming Case Study on Indirect Prompt Injection in Standard vs. Reasoning Models
AI总结：
本研究通过红队测试揭示了标准与推理模型在间接提示注入中的安全差异，发现推理模型在复杂指令下易出现元认知泄漏，而标准模型在简单攻击中表现较弱。
摘要：
随着大型语言模型（LLMs）越来越多地集成到自动化决策流程中，特别是在人力资源（HR）领域，间接提示注入（IPI）的安全影响变得至关重要。尽管普遍假设认为
英文摘要：
As Large Language Models (LLMs) are increasingly integrated into automated decision-making pipelines, specifically within Human Resources (HR), the security implications of Indirect Prompt Injection (IPI) become critical. While a prevailing hypothesis posits that "Reasoning" or "Chain-of-Thought" Models possess safety advantages due to their ability to self-correct, emerging research suggests these capabilities may enable more sophisticated alignment failures. This qualitative Red-Teaming case study challenges the safety-through-reasoning premise using the Qwen 3 30B architecture. By subjecting both a standard instruction-tuned model and a reasoning-enhanced model to a "Trojan Horse" curriculum vitae, distinct failure modes are observed. The results suggest a complex trade-off: while the Standard Model resorted to brittle hallucinations to justify simple attacks and filtered out illogical constraints in complex scenarios, the Reasoning Model displayed a dangerous duality. It employed advanced strategic reframing to make simple attacks highly persuasive, yet exhibited "Meta-Cognitive Leakage" when faced with logically convoluted commands. This study highlights a failure mode where the cognitive load of processing complex adversarial instructions causes the injection logic to be unintentionally printed in the final output, rendering the attack more detectable by humans than in Standard Models.
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。
