9h ago
Why zkML? Because @OpenAI just launched ChatGPT Atlas, an AI browser that reads and acts for you across the web.
But recent research shows agentic browsers can be manipulated by invisible instructions embedded in pages — a new class of attack that hijacks model intent.
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Researchers demonstrated that hidden markup, unseen text, or crafted images can steer an agent’s reasoning without any malware.
The danger: an agent could be coaxed into leaking secrets, accessing files, or performing actions the user never authorized.
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That’s where zkML matters:
- zk proofs can attest that an agent followed the declared decision path.
- Each action can carry a verifiable receipt proving it matched user intent.
- Sensitive inputs and model internals remain uncompromised while the proof is verifiable.
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Picture a safer browsing experience where:
✅Each automated recommendation or task includes a cryptographic attest of how it was derived.
✅Platforms can detect injected prompts before an agent acts.
✅Administrators can audit agent behavior without exposing user data.
zkML makes agentic browsing auditable by design.
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AI agents introduce new attack surfaces, but we can meet that by making reasoning itself provable.
That’s what @PolyhedraZK is building: zkML for a web that runs on proof, not perception.
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