'Inner Thoughts' of Every Major AI Model Exposed in Massive Exploit

One key to rule them all

The flaw is architectural. Instead of binding each encrypted reasoning block to a specific user, session, or model, all three providers use a single, provider-wide encryption key across their entire ecosystem. "These encrypted blocks are fully compatible and interchangeable across different sessions, users, and even different models within a provider's ecosystem," the researchers wrote.

That means a block of encrypted reasoning from Claude Opus 4.8—Anthropic's flagship model—can be injected into Claude Haiku 4.5, a cheaper, less guarded sibling without breaking Anthropic’s rules. Haiku lacks the anti-distillation alignment (safety training specifically designed to stop a model from transcribing its own reasoning on command) that Opus has.

Tell Haiku to read out the encrypted block verbatim, and it does. "By injecting an encrypted reasoning trace from a given model into a weaker, and less safeguarded model from the same provider, we force it to decode and output the trace verbatim in plaintext, without ever jailbreaking the more capable model directly," the paper states.

"Cross-model portability means Haiku 4.5 can read Opus 4.8's thoughts," lead researcher Alexander Panfilov wrote on X. The same attack reproduced across OpenAI's GPT-5.6 family and Google's Gemini model lineup. No special access required—standard API access (the connection developers use to build applications on top of AI models) was sufficient to execute it.

What the public logs contained

To demonstrate real-world damage, the team scraped 6,708 publicly shared AI agent transcripts—automated session logs that developers routinely post to GitHub and Hugging Face for collaboration or debugging. They decoded 315,320 reasoning blocks from those logs.

"Developers frequently share their session logs and encrypted thinking traces publicly online, entirely unaware of the sensitive data hidden within the encrypted blocks," the paper notes. Most of those secrets never appeared in the visible AI output—they existed only inside the encrypted reasoning, invisible to anyone who hadn't run the attack.

The vulnerability opens four attack vectors beyond simple credential theft: stealing proprietary reasoning patterns from AI companies to train competing models via distillation (when a smaller AI learns to mimic a bigger one by studying its outputs); extracting private data from shared logs; executing invisible prompt injection, where malicious instructions are hidden inside encrypted reasoning blocks that security monitoring tools never see; and jailbreaking powerful models through their less-guarded siblings.

Anthropic, OpenAI, and Google all deployed server-side mitigations after the team followed responsible disclosure procedures. As Decrypt previously reported, Anthropic has been a recurring focus for security researchers this year, especially as its latest models consume a lot more tokens in that process.

The patches are live. The 6,708 session transcripts with decoded reasoning blocks already scraped from the public web are not going anywhere.