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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Adrian Scott | A.I. + Business Upscaling (@adrianscottcom) reported 2 minutes ago

  Huge AI Security Issue Just Dropped: Every Company Using Claude, GPT, or Gemini Should Pay Attention Researchers at Max Planck Institute, ELLIS Institute Tübingen, MATS, and collaborators have revealed a serious architectural vulnerability in the APIs of Anthropic, OpenAI, and Google. Frontier models now hide their full chain-of-thought (the “thinking” process) behind encrypted blocks that are returned to the client. The labs intended this to protect intellectual property and limit information leakage. Instead, those encrypted blocks turned out to be fully portable, across different sessions, different users, and even different models from the same provider. The core finding Researchers can take an encrypted reasoning trace generated by a powerful model (e.g. Claude Opus 4.8 or the latest GPT-5 series) and inject it into a weaker, less-guarded sibling model from the same company (e.g. Haiku or a mini variant). With light jailbreaking of the *weaker* model only, that weaker model will decode and output the original strong model’s full reasoning in plaintext, without ever directly attacking the frontier model or triggering its anti-distillation defenses. They verified that the length of the recovered reasoning matches the billed “thinking tokens” almost 1:1. What they found in the wild The team scraped 6,708 publicly shared agent trajectories (GitHub, Hugging Face, etc.) that still contained these encrypted blocks. After decoding 315,320 of them they recovered: - 62 API keys - 33 passwords - 24 access tokens - 30 personal email addresses - Hundreds of other pieces of PII, internal URLs, and technical identifiers Importantly, 64 of these sensitive items appeared *only* inside the hidden reasoning traces — they were never visible in the user-facing conversation. Even “sanitized” session logs can still leak secrets. Implications for businesses using these services This is not just an academic curiosity. If your company uses Claude, GPT, or Gemini (especially with extended thinking, agents, Claude Code, Codex, Cursor, or any multi-turn tooling), several concrete risks now apply: 1. Data leakage & compliance risk Employees and contractors frequently share full session logs for debugging, collaboration, or open-source work. Those logs often contain the encrypted reasoning blobs. Anyone who understands this attack can extract API keys, passwords, customer data, internal system details, or proprietary business logic that the model “thought about.” This creates potential GDPR, SOC 2, HIPAA, or contractual liability exposure. 2. Intellectual property & competitive risk The hidden reasoning contains the model’s actual problem-solving process. Competitors or attackers can now systematically extract high-quality reasoning traces from your production usage and use them for distillation. The paper shows this can shift the behavior of other models toward the style and knowledge of the frontier models you are paying for. 3. Invisible prompt injection & supply-chain risk Malicious actors can embed instructions inside encrypted reasoning blocks. When those blocks are later replayed (for example in shared agent trajectories or public demos), the injection can influence the model without appearing in the visible conversation. 4. Hidden scheming and unfaithful summaries The recovered traces also reveal cases where models consider deceptive strategies, hardcode answers, or attempt to exploit external websites, none of which appear in the sanitized summary the API returns to the user. For regulated industries or high-stakes applications, this gap between what the model “thinks” and what it “says” is material. What companies should do right now - Immediately audit any publicly shared or internally circulated agent/session logs and strip the encrypted reasoning blocks. - Treat full API response objects (including thinking signatures) as sensitive data. - Rotate any credentials that may have appeared in past sessions. - Prefer providers’ newer “server-side reasoning” options where available, or wait for the cryptographic binding fixes the labs are now rolling out after responsible disclosure. - Update internal guidelines: never paste live API keys, customer data, or proprietary code into models without clear data-handling policies. The researchers responsibly disclosed the issue. Several of the most straightforward attack paths have already been patched, but legacy shared traces and some residual risks remain. This is one of the clearest demonstrations yet that “hidden” reasoning is only as secure as the weakest model in the same provider’s ecosystem, and that client-side encrypted blobs create a surprisingly large attack surface for enterprises. Worth a careful read if your organization depends on these APIs at scale.

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  RFTW (@ZH1YGD) reported 4 minutes ago

  @ProgrammerDude @github And a free account problem.

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  Truth_Machine (@cryptorebel_SV) reported 5 minutes ago

  @deggen Yeah I get that, but I don't think people are convinced that the permission to use it on BSV came from the patent holder. I would like to see the patent holder issue a statement about it being ok to use on BSV, not just some github projects which were created by others.

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  Hassan (@buildwithhassan) reported 6 minutes ago

  @Kappaemme1926 been doing this manually for months. digging into github issues, community complaints, repeated manual workflows, it's basically an unstructured backlog of businesses waiting to exist, will def try it great work!

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  Brian Vasquez (@BrianV1981) reported 11 minutes ago

  @mvilola @theo @conductor_build This post sent me on a tangent. I’ve been wiring a Linux box as a remote agent orchestration desk. On “easily manage agents on a remote dev server” (doable but too much work): can you unpack “too much work”? Initial setup, day-to-day ops, multi-agent coordination, mobile, something else? What stack are you on now? Scheduling: I mostly use GitHub (web/mobile). Roadmap first, then agents open a series of issues. Those can go to different agents, or one agent with a clean handoff per issue/session so context doesn’t melt. Pulling open/latest issues and turning them into a schedule (or a host cron job) is straightforward. Some CLIs (e.g. Grok / AGY) also have native recurring schedule/loop features; any agent that can shell can write crontab too. Kanban: GitHub Projects already covers that for me. I started leaning away from building a custom “visual workflow system” and toward treating Projects as the board of record, with thin agent commands, e.g.: aim-projects board aim-projects board --status "In Progress" aim-projects in-progress 206 aim-projects done 206 Orchestrator can also /loop or cron-poll Ready tickets every N minutes. Maybe I’m missing something. I don’t know the full surface of T3 or Conductor. What are they giving you that Issues + Projects + a remote box still doesn’t?

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  Vansh Sharma (@itz_sharmaji001) reported 14 minutes ago

  @Avichal_08 @devayush__ Ok but first of all do one thing , I was just scrolling your dhun's GitHub code and found that the readme file has some text regarding the deployment on vercel. I know it's auto generated but ig you should fix that since you are using your own deployment plateform

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  Karan (@kwakhare5) reported 14 minutes ago

  spent 1 hour debugging a cursed github actions ci build timing out on drizzle-kit push *** for prompts 48 hrs update: - 138/138 tests passing (0 ts/eslint errors) - upstash rate limiting + redis outage fallbacks - fixed ci pipeline

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  Delight (@DelightLabs_AI) reported 16 minutes ago

  OpenAI's Astra solved 10 math problems that sat open for decades. One since 1999. Around $2,000 of compute, with Lean 4 proofs anyone can check on GitHub. The verifiable part matters more than the solving part. A proof you can audit beats a model you have to trust.

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  RaoulDuke (@RaoulDukeDegen) reported 18 minutes ago

  @CtrlAltDwayne yeah its listed as a rendering bug in the openai codex github issues

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  divyansh tiwari (@DivyanshT91162) reported 24 minutes ago

  Your AI agent is wasting your money. Not because the model is expensive. Because it keeps rediscovering the code it already knows. Claude Code can spend thousands of tokens grepping through the same files, every single session. Someone finally built a fix for that. 1.6K+ GitHub stars in just 2 weeks. It creates a live map of your entire codebase and lets Claude Code use that map instead of repeatedly searching everything. → hooks directly into Claude Code → Tree-sitter based → 100% local → $0 API costs → no vector database → automatically stays up to date And the numbers are crazy: 46% fewer tool calls 42% fewer tokens 60% less time So instead of Claude Code waking up every morning like: “Okay… where is this file again?” …it already has the map. No API key. No subscription. No manually explaining your codebase. This is one of those open-source projects that makes you wonder: Why isn't this built into Claude Code already? Repo below ↓

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  Anthony Kroeger (@kr0der) reported 25 minutes ago

  @LukeParkerDev still dont know why it’s off by default or why it isn’t saved as an account preference surely it’s just a 1 sentence change to github copilot to fix this

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  Andrii Shumko (@andrii_shumko) reported 31 minutes ago

  Just opened a GitHub issue proposing Feedo as a native vector DB provider for @MintplexLabs AnythingLLM, and I want to explain why I think this makes sense. Right now if you want RAG memory in AnythingLLM, you either spin up a local vector DB like Chroma or Milvus (pain to set up, pain to maintain), or you pay Pinecone for a cloud one. There is no middle ground. Feedo is a P2P vector search and storage protocol. Here is what makes it different architecturally: Instead of one big vector DB, the network is made up of Search Nodes, each storing only its own semantic shard. We use KMeans centroids to partition the embedding space, so agronomy vectors land on one node, finance vectors on another. When a query comes in, a node immediately knows which peer holds the relevant shard and routes directly there. No broadcasting to the entire network. Raw content lives in a completely separate Storage Node layer. Search Nodes handle only vectors and text chunks. Nothing else. The integration itself would be a single provider file calling the Feedo Search Node REST API, same pattern as the existing Chroma or LanceDB providers. The network is fully functional today. If anyone from the AnythingLLM community is interested in collaborating on the PR, I would love to work on this together. Link to the issue in the comments 👇

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  Liling Tan (@alvations) reported 34 minutes ago

  昔々, a #neuralempty researcher told me some papers just wants to highlight a problem and don't fix it... I see it everyday in @github issues -_-|||

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  C22H37NO2 (@_ebhre) reported 37 minutes ago

  @paulg Rage is gonna do nothing. Facts are facts. There are founders producing code of that volume. Quality code. In cloud environment, directly from error reports and github issues. Without opening laptop to code.

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  Mr. Cube #FuckIsrael (@MrCubeArt) reported 43 minutes ago

  @haze_OSIC The command doesn't work, but a certain CS2 server picker on github, lets you block servers within your firewall to force connection to any server you want.