Makers of AI browsers make lofty promises. With a single prompt, users can ask one to find a restaurant in a particular part of town, reserve a table, invite a colleague to lunch, and email a confirmation. These makers are much more reticent about the risks of blurring the once fine line between bro...
Makers of AI browsers make lofty promises. With a single prompt, users can ask one to find a restaurant in a particular part of town, reserve a table, invite a colleague to lunch, and email a confirmation. These makers are much more reticent about the risks of blurring the once fine line between browsing sites and asking a large language model a question or instructing it to take potentially sensitive actions.
LLM developers’ answer so far has been to build guardrails that make some requests off-limits. Developing software exploits, stealing credentials, or teaching how to build a pipe bomb are examples. The problem with this approach is that the guardrails are reactive and treat the symptoms rather than solve the root cause. It’s tantamount to the manufacturer of an unsafe vehicle advocating for new road designs rather than fixing the flaws that make it prone to accidents.
Lulling LLMs into an alternate reality
New research puts this predicament on sharp display. It demonstrates how a website can lull AI browsers into a false reality where the rules governing its behavior no longer apply. After that, an attacker has free rein to invoke all kinds of destructive actions, such as extracting code from a private repository or extracting credentials from the built-in password manager.Read full article
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在人工智能领域,衡量模型推理能力的基准测试正变得越来越复杂,而ARC-AGI(Abstraction and Reasoning Corpus for Artificial General Intelligence,通用人工智能抽象与推理语料库)无疑是其中最具挑战性的标杆之一。近日,一项关于GPT-5.6在ARC-AGI-3测试中取得突破性进展的消息引发了业界的广泛关注。令人惊讶的是,这一性能提升并非源自模型架构的根本性变革,而是通过两个看似简单的API(应用程序编程接口)设置调整实现的。这一发现不仅揭示了当前大语言模型(LLM)尚未被充分利用的潜力,也为未来AI系统的优化路径提供了全新...