This is the natural consequence of building everything around "the agent needs access to everything to be useful." The more capabilities you hand an agent, the larger the attack surface when it encounters a malicious page.

The simplest mitigation is also the least popular one: don't give the agent credentials in the first place. Scope it to read-only where possible, and treat every page it visits as untrusted input. But that limits what agents can do, which is why nobody wants to hear it.

I absolutely agree, although even that doesn't solve the root problem. The underlying LLM architecture is fundamentally insecure as it doesn't separate between instructions and pure content to read/operate on.

I wonder if it'd be possible to train an LLM with such architecture: one input for the instructions/conversation and one "data-only" input. Training would ensure that the latter isn't interpreted as instructions, although I'm not knowledgeable enough to understand if that's even theoretically possible: even if the inputs are initially separate, they eventually mix in the neural network. However, I imagine that training could be done with massive amounts of prompt injections in the "data-only" input to penalize execution of those instructions.

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