Show Notes
Labs have spent the week racing to ship AI agents that browse the web and act on what they find. Today, the first systematic measurement of how badly that can go arrived: a research paper showing that adversarial web content can corrupt AI search agents' recommendations at rates as high as 31 percent — and that safety performance at the recommendation layer doesn't predict safety when the agent is asked to take action. The real risk isn't that your assistant gets fooled once; it's that bad actors learn to treat the web itself as an attack surface for shaping AI-mediated decisions at scale.
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