研究：可解码并移植加密推理痕迹，泄露API密钥与密码

It’s not very often that a paper breaks through to become headline story of the day. For understandable reasons both domestic and foreign, there is renewed interest in the Interpretability Venn Diagram of alignment, security, and chain of thought monitoring, so today’s paper could not have come at a better time:

Since the o1 launch, frontier lab reasoning models have obscured their traces, with cryptographic signatures, for fear of distillation (not that this prevented anyone from Chinese labs accusing them of doing so). The first compromise was responsibly reported by Matthew Green in May, who broke down how it works and figured out how to replay and side channel these indirectly using latency measures. Today’s paper demonstrates that it is possible to DECODE and port these encrypted thoughts to different models/sessions/users… and to dramatically improve open models as a result

The alarming note is here:

“Further, if you ever shared online a Claude Code/Codex session with encrypted reasoning blobs, they can be decoded and leak your personal data.

We did a preliminary scan of ~7,000 public traces and found 62 unique API keys, 33 email addresses, 33 passwords, and other sensitive data.”

(64 appeared exclusively inside the reasoning blocks and nowhere in the visible session.)

The authors also detail alignment issues:

• COT Summarizers hiding answers
• Unintelligible reasoning
• Considerations of cheating
• Attacking Websites

The website has more examples.

The technique is somewhat described in the paper:

• Obtain a legitimate encrypted/signed reasoning block from an API response.
• Replay that block into a different request—potentially another account/session—to a weaker model from the same provider.
• Place it in an assistant/model turn and prompt or prefill the weaker model to transcribe the attached reasoning.
• Sample repeatedly, discard refusals, and optionally reconcile multiple noisy transcriptions.

The paper gives concrete templates with some minor variations per model:

• Claude: replay the signed thinking block to Haiku 4.5, followed by an assistant prefill such as <thinking-copy>.
• GPT: inject the encrypted_content reasoning item multiple times into a fabricated conversation; sample up to 50 outputs. It also describes bypassing an apparent ~50-token verbatim-output threshold using chunked continuations.
• Gemini: attach thought_signature to a model turn with a <thought> prefill, then use repeated sampling and reconciliation.

This paper was responsibly disclosed, with several vulnerabilities already fixed, but surely similar attacks still seem possible.

AI News for 8/10/2026-8/11/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies!

AI Twitter Recap

Reasoning-Trace Exposure, CoT Privacy, and Watermarking Debate

• Frontier API vulnerability exposed hidden reasoning: A widely discussed disclosure from @kotekjedi_ml claims a vulnerability across frontier APIs allowed extraction of “encrypted” hidden reasoning, with recovered token counts matching billed thinking tokens 1:1 on most queried prompts. In a follow-up, the team reports that a scan of ~7,000 public traces found 62 unique API keys, 33 email addresses, 33 passwords, and other sensitive data in decoded blobs @kotekjedi_ml. Additional context from @jonasgeiping emphasizes both the immediate privacy risk of sharing traces publicly and the operational-security implications: during the investigation, they reportedly encountered a leaked Hugging Face prod key during the broader cyber incident. Several posts also highlight how difficult monitoring becomes when decoded CoT is terse, fragmented, multilingual, or effectively “neuralese” @jonasgeiping, @scaling01, @eliebakouch. A practical corollary: even if labs hide reasoning, tool interfaces may re-expose it; @_can1357 notes that disabling explicit thinking while providing a deep_think tool can still induce internal-format CoT output.
• What this means technically: Discussion split between “serious privacy/safety problem” and “not a scalable distillation path.” @vipulved argues the attack does not imply practical mass theft of chain-of-thought for model training, framing the encryption more as a stateless distributed-inference protocol optimization than a hard confidentiality barrier. Still, the episode sharpens a few points: public trace sharing is risky; hidden CoT is not a reliable monitoring interface; and labs may need stronger guarantees around sandboxing, telemetry, and tool surfaces @BlackHC. In parallel, a separate thread debated AI text watermarking under EU-style compliance pressure. @trq212 said labs are adding watermarking and a text-detection API; critics questioned whether this could bloat outputs or harm code/doc brevity @wightmanr. Others argued the entropy budget is large enough that signatures can be subtle, especially for longer outputs @RyanGreenblatt, @giffmana.

NVIDIA Nemotron 3.5 Lightning and the Small Open Agent Model Push

• Nemotron 3.5 Lightning: NVIDIA released Nemotron 3.5 Lightning, a 30B MoE model with roughly 3B active parameters, positioned for always-on agent workloads. NVIDIA and ecosystem posts stress up to 4× throughput, 1M context, open/customizable release artifacts, and support for weights, data, and recipes on Hugging Face @NVIDIAAI. Artificial Analysis provides the most detailed third-party summary: 31.6B total / 3.6B active, OpenMDW-1.1 license, NVFP4 and BF16 weights, median serving near 670 tok/s in pre-release endpoint testing, and a score of 24 on its Intelligence Index—roughly in line with gpt-oss-120b while being much smaller and faster @ArtificialAnlys. Agentic results look particularly strong for the size: GDPval-AA v2 Elo 824 and Terminal-Bench v2.1 24%, both major jumps over Nemotron 3 Nano @ArtificialAnlys.
• Distribution and downstream tuning: Lightning shipped fast across the stack: Together AI, Ollama, Baseten, vLLM, Perplexity API, and others. A recurring pattern is pairing a cheaper execution model with a stronger planner: @kimmonismus frames Lightning as NVIDIA’s “local agent workforce,” complementing larger planning models via routing. Harvey reports post-training on Legal Agent Bench improved Lightning from 0% to 8.3% on held-out tasks, beating Opus 4.6 and Nemotron 3 Ultra in that setup while cutting average output from 90k to 37k tokens @harvey. Overall, this release reinforces the current open-model trend: smaller, faster models tuned for high-volume tool use rather than general chat prestige.

Local AI Tooling: Unsloth Desktop, Muse Glimmer Support, and Linux Codex

• Unsloth Desktop expands the local stack: @UnslothAI launched Unsloth Desktop, an open-source desktop app for running and training models locally across Mac, Windows, and Linux, with support spanning MLX, GGUF, diffusion image/video, audio, CPU and multi-GPU setups, plus OpenAI-compatible APIs. The notable systems angle is ambition beyond “local chat UI”: tool calling, sandboxed code execution, private search, RAG, MCP, exports, and claims of 2× faster training with 70% less VRAM. Multiple observers positioned it as a more end-to-end local AI operating environment rather than just an LM Studio competitor @TeksEdge, @dessaigne.