Near AI breach exposes user details and data from 18 AI agents
A flaw in the SGLang inference software let an attacker into Near AI's internal systems, raising questions about how far hardware-backed privacy can really go.
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Key takeaways
- An SGLang flaw gave an attacker access to Near AI's internal systems.
- User details and data tied to 18 AI agents were exposed.
- Confidential AI requests could potentially have been viewed.
- The incident shows the limits of hardware-backed privacy protections.
Eighteen AI agents had their data exposed. So did user details. An attacker got into Near AI's internal systems through a flaw in SGLang, according to a Decrypt report published November 10, 2026.
What we know
The way in was SGLang, software that serves AI models. One flaw there, and the attacker reached internal systems. Confidential AI requests could potentially have been viewable too.
The details are thin. The source doesn't say how many users were hit, who the attacker was, or how long the access lasted. I'm not going to guess. Neither should you.
The privacy problem
Near AI has leaned on hardware-backed privacy protections. The idea: keep user prompts shielded even from the people running the service. This breach shows where that idea runs out. Secure hardware doesn't do much if the software wrapped around it has a hole.
That's the lesson. Privacy is only as strong as the weakest layer in the stack.
Why it matters
Crypto's pitch for AI is often private, verifiable, decentralized compute. Nice pitch. But if you're feeding sensitive prompts, strategies or wallet-related data to an AI agent, treat "private" as a claim to test, not a guarantee. Ask what software sits between you and the secure hardware. Ask who audits it.
It also lands on the growing market for on-chain AI agents. If one flaw can expose data from 18 agents, how many other platforms share the same dependency? Expect that question to get louder.
Source: Decrypt
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