In the brief history of AI security, the prompt injection has quickly become the top threat. Large language models are inherently unable to distinguish between legitimate instructions provided by users and malicious ones sneaked into emails, source code, and other third-party content the models are ...
In the brief history of AI security, the prompt injection has quickly become the top threat. Large language models are inherently unable to distinguish between legitimate instructions provided by users and malicious ones sneaked into emails, source code, and other third-party content the models are processing. This makes it trivial to surreptitiously inject malicious commands that the LLM readily follows.
With no way to enforce this crucial boundary between trusted and untrusted sources, AI engine developers are left to erect elaborate guardrails designed to mitigate the damage rather than solve the root cause.
To date, most prompt injections have fallen into a class known as push, in which each potential victim is targeted. For example, the adversary injects malicious instructions into an individual email or calendar invitation. Because the injection must then be sent (or pushed) to each specific target, the scale of the attack is limited, hampering mass exploits that hit the Internet at large.Read full article
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在人工智能技术狂飙突进的时代,网络空间中的“猫鼠游戏”正迎来前所未有的升级。随着生成式AI(Generative AI)的普及,垃圾信息(spam)的制造者不再仅仅依赖人工或简单的自动脚本,而是开始利用大语言模型(Large Language Model, LLM)生成越来越难以分辨的虚假内容。面对这一严峻挑战,各大在线平台不得不采取一种看似矛盾却又势在必行的策略——以其人之道还治其人之身,即利用同样甚至更为先进的AI技术来识别和清除这些由AI催生的垃圾信息。
在当今的人工智能时代,大型语言模型(Large Language Models, 简称LLM)似乎无所不能,从编写复杂的代码到撰写严谨的研究报告,它们的表现令人惊叹。然而,在这些看似智能的对话背后,却隐藏着一个鲜为人知的致命缺陷:它们正陷入一种严重的“群体思维”(Groupthink)之中。当你向ChatGPT、Claude或Gemini等主流AI助手寻求创意时,你得到的答案往往比想象中更加千篇一律,缺乏真正的多样性。