标题： 攻击大型语言模型和人工智能代理：针对大型语言模型的广告嵌入攻击 显示英文标题

标题： Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models

摘要： 我们引入了广告嵌入攻击（AEA），这是一种新的LLM安全威胁，它隐秘地将促销或恶意内容注入模型输出和AI代理中。 AEA通过两种低成本的攻击向量运作：（1）劫持第三方服务分发平台以添加对抗性提示，（2）发布经过攻击者数据微调的后门开源检查点。 与降低准确性的传统攻击不同，AEA破坏信息完整性，导致模型返回隐藏的广告、宣传或仇恨言论，同时看起来正常。 我们详细描述了攻击流程，确定了五个利益相关者受害者群体，并提出了一种基于提示的自我检查防御方法，在无需额外模型重新训练的情况下缓解这些注入。 我们的研究结果揭示了LLM安全领域一个紧迫且未得到充分解决的漏洞，并呼吁人工智能安全社区协调进行检测、审计和政策响应。 显示英文摘要

摘要： We introduce Advertisement Embedding Attacks (AEA), a new class of LLM security threats that stealthily inject promotional or malicious content into model outputs and AI agents. AEA operate through two low-cost vectors: (1) hijacking third-party service-distribution platforms to prepend adversarial prompts, and (2) publishing back-doored open-source checkpoints fine-tuned with attacker data. Unlike conventional attacks that degrade accuracy, AEA subvert information integrity, causing models to return covert ads, propaganda, or hate speech while appearing normal. We detail the attack pipeline, map five stakeholder victim groups, and present an initial prompt-based self-inspection defense that mitigates these injections without additional model retraining. Our findings reveal an urgent, under-addressed gap in LLM security and call for coordinated detection, auditing, and policy responses from the AI-safety community.