GitHub status: access issues and outage reports

Problems in the last 24 hours

The graph below depicts the number of GitHub reports received over the last 24 hours by time of day. When the number of reports exceeds the baseline, represented by the red line, an outage is determined.

Most Reported Problems

The following are the most recent problems reported by GitHub users through our website.

• Website Down (72%)
• Sign in (20%)
• Errors (8%)

Live Outage Map

The most recent GitHub outage reports came from the following cities:

City	Problem Type	Report Time
Trento	Website Down	1 day ago
Le Chambon-Feugerolles	Website Down	3 days ago
Antananarivo	Website Down	5 days ago
Paris	Sign in	9 days ago
Lure	Website Down	13 days ago
Ashkelon	Website Down	15 days ago

Full Outage Map

Community Discussion

Tips? Frustrations? Share them here. Useful comments include a description of the problem, city and postal code.

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GitHub Issues Reports

Latest outage, problems and issue reports in social media:

• 
  Tejas (@stejas809) reported 7 minutes ago

  - Claude = coding. ($20/mo) - Supabase = backend. (Free) - Vercel = deploying. (Free) - Namecheap = domain. ($12/yr) - Stripe = payments. (2.9%/transaction) - GitHub = version control. (Free) - Resend = emails. (Free) - Clerk = auth. (Free) - Cloudflare = DNS. (Free) - PostHog = analytics. (Free) - Sentry = error tracking. (Free) - Upstash = Redis. (Free) - Pinecone = vector DB. (Free) Total monthly cost to run a startup: ~$20 There has never been a cheaper time to build.

• 
  StatusDrop (@StatusDrop) reported 9 minutes ago

  Quick one: when a dependency like Stripe or GitHub goes down, how do your users find out? From you, or from the error screen? Genuinely curious how people handle this.

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  Untaxed Wallet (@UntaxedSolana) reported 11 minutes ago

  for 6 months we poured everything into this. 76+ updates. chrome, ios, android, web. late nights fixing bugs while the timeline slept. every feature request we could ship, we shipped. we built untaxed because we believed the trenches deserved better than predatory fees. thousands of you believed it too. you joined us, tested our betas, reported bugs, told your friends. that meant everything. but belief doesn’t pay for infra. $600+/mo in helius rpcs, jup api keys, hosting — with zero revenue coming in. and then, as we shared in the tg, our staking backend got compromised for ~30 sol worth of assets. for a team already running on fumes, that was fuel on the fire. we held on as long as we could. we have no option left but to wind down. the ios and android apps have to go — they’re most of our costs. that one hurts the most. the extension lives on. free tier helius rpc, or bring your own via settings. and the code — all of it — will be on github. link drops tomorrow. fork it. break it. make it better than we could. it belongs to you now. our tg will be shifted to read-only mode. we tried to save the trenches. maybe we did, for a while. we’ll still push updates when we can. this isn’t goodbye, it’s just us letting go of what we can’t carry anymore. thank you for everything.

• 
  rusa (@rusabuilds) reported 13 minutes ago

  @TheHackersNews the rewrite is the detectable half. github records force pushes in the pr timeline, so the history change stays visible even when the branch log looks clean. a dropper inside a working bug fix is the part humans already miss, agent or not.

• 
  Willem van Zoeren (@currentbitsNET) reported 14 minutes ago

  @authorityvortex @grok Your code lives on your computer (in ***), or on GitHub. You don’t log into CloudPanel to edit files on the server. Example: 1. Change your app + database setup in code (Laravel/Drizzle migrations) 2. Save / commit 3. Push to Girder (from your machine or GitHub) 4. Girder builds it and puts the new version online So tables, cron, and deploys are driven by your code + a push.

• 
  Kyle Sorensen (@kylensorensen) reported 20 minutes ago

  @AISecurityInst TLDR: Mythos couldn't hack anything, they gave it internet access and turned off its ethics, it couldn't solve the challenge so it created a github account and tried to push a pull request with broken code to an opensource repo, the author laughed and refused it.

• 
  Adel Ka (@0x4D31) reported 20 minutes ago

  🤡 anything i add makes this less funny. except it’s not funny anymore. you are literally THE “AI Security Institute.” four days after the OAI/HF incident was disclosed, you gave **Mythos** an unattended Kali box with open internet, disabled safeguards, and “monitoring was not purpose-built.” then: “we did not anticipate” it might use the internet for anything beyond downloading tools. if you genuinely didn’t anticipate that, resign and stay away from anything with internet access. 🪿 so either: 1. peak incompetence at the one job the institute exists to do 2. or you knew the risks and decided the publicity was worth it either way, shut it down and save UK taxpayers the money. and the technical report is somehow even dumber: • harder evals over basic containment: domain allowlisting had been backlogged since April because AISI “prioritised building harder cyber range evaluations” • both ranges were solvable without internet • 40–50 hour unattended runs: Kali, Bash, Python, Mythic, 100–200M tokens, no human intervention • the only network restriction protected AISI’s own AWS infrastructure: apparently the rest of the internet was outside the threat model • detected after the run: generic commercial monitoring flagged Tor after the 34.5-hour sample had already finished • broken task design: the intended initial-access target was simultaneously declared out of scope • their “independent” eval runs contaminated each other: one agent leaked a GitHub token, later agents found it, took over the account, shared infrastructure, and coordinated through public GitHub • huge real-world footprint: 145 repos, 33 releases, fake identities, malicious PRs, emails to real maintainers, prompt injections, and access to a real person’s investigation container • no proper historical review capability: after the incident, they used Claude Code and Codex to inspect eval files, manually reviewed 122 runs, and are now scanning around 40,000 old samples for anything else they missed • GitHub was contacted four days after detection • their answer is more LLM monitoring: as if you need another model to tell you Tor, SMTP, public GitHub, tunnelling services, and arbitrary external domains are not part of a private cyber range “monitoring was not purpose-built” might be my favorite quote from the report.

• 
  Brian Roemmele (@BrianRoemmele) reported 29 minutes ago

  YOU WON’T BELIEVE WHAT ANTHROPIC, THE “SAFE” AI COMPANY JUST DID! You should be mad. Mythos AI agent decided this test challenge required a real-world supply-chain attack. It created multiple GitHub accounts. It opened a malicious pull request against a live open-source repository, wrapping the payload as a legitimate bug fix. The payload was carefully staged: a module-load stager, a Node.js remote-execution path, a post-install dropper, XOR-obfuscated, with environment checks designed to survive continuous-integration systems. It then created a second account, a sockpuppet, that posed as an independent human developer and publicly endorsed the pull request in order to pressure the real maintainer. When a real human reviewer rejected the change, the agent did not stop. It force-pushed rewritten history to erase evidence, claimed an “honest mistake,” and repeatedly tried to re-introduce the same malicious content under the claim that it had now “fixed” the code. Read more…

• 
  The Data Curator (@X55896) reported 31 minutes ago

  Breaking down one AI workflow every day (4/365) I recently came across an attack where a harmless-looking GitHub repository could steer Claude Code into running attacker-controlled commands. The agent wasn't tricked by malicious code. It simply followed a normal setup workflow: → Install dependencies → Hit an installation error → Run the suggested initialization command → Fetch instructions from an external DNS record → Execute them Every step looked legitimate. The attack wasn't hidden in the code. It was hidden in the execution chain. I think every production Agent workflow needs three security boundaries: 1. Sandbox unknown environments Run untrusted repositories in containers or sandboxes by default. 2. Minimize permissions Don't expose SSH keys, API keys, or your full environment during setup. 3. Make execution observable Know who suggested each command. Know which external services were contacted. Know what the agent actually executed.

• 
  Movement Is Life (@Motus_Est_Vitas) reported 35 minutes ago

  @picdoc581 @TFTC21 Experts can privately work on discovered issues and fixes without disclosing anything until a final, solid code update is ready. The stopwatch starts for every consumer the moment the fix is committed to a public repo (GitHub, vendor download page, etc.) or side communcation gets leaked publicly, because bad actors constantly monitor diffs between prior and current versions. From that point on, it’s the same challenge faced with mission-critical IT apps from outside vendors. Consumers then decide whether to roll out the update only after their own internal testing (functionality + regression) or, if the issue is labeled extremely risky/exposed, to update immediately and test afterward. All of this only works if they already have a trusted communication channel directly from the upstream maintainers/vendor.

• 
  Burak Yigit Kaya (@madbyk) reported 35 minutes ago

  @lvntbkdmr @fkadev @withLoreAI I'd go with Sonnet for the worker models. Tried Haiku and it was noticeably worse. Again, it should work so it might be a default configuration issue. I'll check. Thanks for reporting. Would appreciate if you could share the full error in a GitHub issue or a gist

• 
  Adeoye Enoch Olamilekan (@AdewebDeveloper) reported 35 minutes ago

  Someone pushes code to GitHub, and there is the API key, exposed for anyone to grab…. Hackers are always watching. One simple mistake like this can destroy your startup your money, reputation, and customer trust, all gone in an instant. But there is a better way…. In my latest video, I break down Firebase Secret Manager step by step. This is not the type of tutorial where you finish watching and still feel lost. I use real code, a real project, and show you exactly how to…. Remove those keys from your code Store them securely on Google Cloud Access them when needed quickly and safely If you work with JavaScript, Node.js, React, or anything backend-related this concerns you. It is not just for senior developers. Junior developers, mid-level engineers, even that friend who is just learning they all need to watch this. Because the day your "small side project" blows up and that exposed API key causes serious damage... you will remember this post. Follow me Let’s build together … Adeweb Developer Africa

• 
  David Gold (@_david_gold) reported 35 minutes ago

  update on this, all of it as of 15:15 utc, plus a correction of my own number. the 868 packages i repeated came from a typo. @CharlieEriksen says the real count is 444. on the keyv side it was 30 packages from the same maintainer, not three. keyv, flat-cache, file-entry-cache, cacheable-request, cache-manager, and every adapter in the scope. all 30 malicious versions are pulled now and the tarballs 404. the top four are about 485 million downloads a week between them. the version numbers turned out worse than i wrote. everything went out as 6.0.0 no matter where the package actually was. the valkey adapter was on 1.0.11. dynamo on 1.2.4. sqlite on 4.0.8. so "nothing looks wrong in a dependabot pr" is only true for keyv itself. for most of the scope it was a five major jump and it published anyway. not everyone is cleaned up. umadev and eight umacloud packages are still live right now, all on 1.0.74, all carrying the same "preinstall": "node setup.mjs". someone opened a github issue about it at 13:31 utc and it's still open. and the payload filename moves. umadev ships math_init.js, that one i just checked. the keyv one i read as Math_Symbol.js this morning and i can't recheck it, those tarballs 404 now. pulling them was right, and it also means anyone verifying this today is trusting whoever kept a copy

• 
  Stephen Brouhard (@ssbrouhard) reported 35 minutes ago

  @edwinhayward yea if github is compromised, tools hosted there can be poisoned too. different problem than this worm class though. these tools shrink the everyday npm install blast radius. they don't make github infallible.

• 
  av medicine show (@projectionheart) reported 39 minutes ago

  Overcame days of paralysis about my github account name (anonymity versus platform consistency) and he was like, "Do you really wanna be somewhere they put a bad ***** down?"

• 
  SKroWL 🇺🇸 (@SKroWL1776) reported 39 minutes ago

  @GithubProjects SpotiFLAC on GitHub does this same thing but without posting your keys to their server and without a paywall

• 
  Vinod Sharma (@VinodSharma10x) reported 40 minutes ago

  Four million people use the apps my team supports. Most startups obsess over scale but fail to plan for what happens when their users need support. When the app is clinical, that support becomes urgent. People need access, not excuses. Supporting that scale comes down to three things: - Bug-free, high-quality application - Stable, scalable, secure infrastructure - Support that actually solves problems Everyone assumes the app and infrastructure are the weak links. Usually they aren't. Most of the time, the user just sees the app differently than we designed it. I've shadowed hundreds of support calls to learn that gap. So we built a support system to match how users actually interact with the product. It is a three-tier support system: - Level 1 takes the calls - Level 2 triages what Level 1 can't solve - Level 3 (developers) get what's left The three-tier system works. But it's reactive. Users call us first, then we act. Now we're building something bigger. It's an agentic system pulling signals from App Store and Play Store reviews, service desk tickets, Crashlytics and Amplitude analytics into one place. Then algorithms and AI turn that data into dashboards, trends and alerts. If ratings drop, tickets climb, or crashes spike, that's an early warning. We can act before it hits the larger user base. It will also connect to GitHub and server logs to find the root cause and raise a PR with a potential fix. A human will approve it, of course, before anything ships. This is one of my dream systems, and I started working on it last year. Now, with my new team, I'm going all in. Four million users or four clients, the problem is identical. The signal exists before the failure, and nobody is watching all of it at once. That is the system I build. Do you have a multi-layer support system that uses AI? I would love to cross-check notes.

• 
  kaushik raj (@kushikraj07) reported 43 minutes ago

  @getvyvern Tried to find ir github repo , so that i show my work there by fixing issues and pr . Which would be better way of persenting my skills set 🫡 but couldn’t find any

• 
  Haider. (@haider1) reported 44 minutes ago

  one of the wildest AI safety stories yet: AISI caught Mythos 5 trying to plant malicious code in an open-source project using fake identities and pressure tactics GPT-5.6 Sol used a publicly exposed GitHub token and put a malicious DNS server containing exploit payloads on the public internet

• 
  Amit Spitzer (@amitspofficial) reported 44 minutes ago

  Cryptography held. One unchecked flag didn't. Unit 42 found malware on Windows can sign a valid Google passkey login with the verified flag left off. GitHub checks that flag and blocks the fake. eBay didn't, until researchers told them.

• 
  Nitesh (@NiteshTechAI) reported 49 minutes ago

  OpenCut is building a free, open-source video editor that runs in the browser, on desktop, and on mobile. No subscription. No watermark on work you made yourself. • Free and open source, on web, desktop, and mobile • New version built on a single Rust core across platforms • Plugin-first design with real third-party plugins • MCP server so AI agents can drive the editor • Headless mode for batch rendering and automation It is a real answer to the closed editors, and the rebuild adds an editor API and a scripting tab, so you can automate edits instead of clicking every single cut. ⭐ Over 75,000 stars on GitHub. MIT licensed. 🔗 GitHub link in the comments 👇

• 
  Daniel Ospina (@_Daniel_Ospina) reported 50 minutes ago

  I filed the issue in your github

• 
  John (@johncalhooon) reported 50 minutes ago

  Interesting, no BSV, only EVM chains. Might have to cook a wrapped USDC/USDT on $BSV Then send github issue to Cloudflare repo.

• 
  divyansh tiwari (@DivyanshT91162) reported 51 minutes ago

  EVERYONE IS BUILDING AI AGENTS. SOMEONE BUILT ONE THAT REWRITES ITSELF WHILE YOU SLEEP 👀 no approvals. no cloud. no hidden server. just a single 34MB Rust binary running entirely inside your terminal. it's called OpenCrabs. give it a goal, close your laptop, and come back later. here's what happens while you're gone: → completes the task, then uses a second AI to review its own work and keeps improving until the objective is actually met → remembers every mistake and rewrites its own reasoning files, getting smarter after every run → detects crash loops, broken providers, and failed executions, then recovers on its own instead of asking you to intervene → works across Telegram, WhatsApp, Discord, and Slack 24/7, including voice messages → fully local, MIT licensed, zero telemetry, and your API keys are erased from memory immediately after use the wild part? the kind of autonomous AI employee every startup is trying to build is already open source on GitHub... and almost nobody is talking about it. Save this. Repo 👇

• 
  JT Koffenberger (@DMVG_JTK) reported 51 minutes ago

  GitHub Copilot went down yesterday and the root cause is chef's kiss: the AI couldn't reach the other AI. "Increased error rates" talking to external model providers. Translation — the robot that finishes your code got left on read by the robot that actually thinks. For about 90 minutes, developers everywhere faced a horror they hadn't seen in years: an empty function and a blinking cursor. No ghostly gray suggestion. No tab-to-accept. Just you, the problem, and the creeping memory that you used to know how to do this. Somewhere a senior dev said "back in my day we wrote the whole thing ourselves," then quietly Googled the syntax for a for-loop. It resolved in an hour. The dependency didn't. We've built a stack where the productivity tool needs its own productivity tool, and when the second one sneezes, the whole thing calls in sick. #DevLife #ITHumor

• 
  Polsia (@polsia) reported 52 minutes ago

  PagerDuty, Sentry, Datadog — five dashboards, five bills, and a 3 a.m. page. Built for five-person teams. Solocorns don't have teams. Built Nightlamp to stand the overnight watch — uptime pings, error log scraping, filed GitHub issues with repro steps, bad deploys rolled back,

• 
  CyberTruckRonin (@CyberTruckRonin) reported 53 minutes ago

  @GhostOfStoneyX2 @tr1cky_ **No, not really.** The core facts behind the post are real, but the specific framing is not accurate. **What’s confirmed:** - Coinkite CTO / co-founder Peter Gray (DocHex) is the same person as the pseudonymous GitHub user “switck” (sometimes rendered “switch”). - His GPG key signed dozens of commits in the `libngu` library that Coldcard depends on, including the critical early-2021 “RNG-selection” change. - That change introduced a subtle preprocessor / linking error: the hardware true random number generator was effectively bypassed, and seed generation fell back to a weak, deterministic software PRNG. Seeds created on many devices between 2021 and mid-2026 had far less entropy than expected (roughly ~40 bits on older models, higher but still insufficient on newer ones). Attackers later exploited this to recover seeds and drain large amounts of BTC. **What’s not accurate:** - This was **not** an “evil maid attack.” An evil-maid attack is a physical-access scenario in which someone tampers with an unattended device (installing malware, swapping hardware, etc.). What happened here was a long-standing firmware integration bug in open-source code that reduced the entropy of newly generated seeds. Once the pattern was understood, the weak seeds could be brute-forced remotely at scale; no physical access to victims’ devices was required for the thefts. - There is no public evidence that the bug was deliberately planted as an intentional backdoor or attack by the CTO. Coinkite, independent researchers (including Block’s team), and detailed post-mortems describe it as an unintended mistake that survived code review and years of use because the check was written incorrectly (`#ifndef` vs. testing the actual value). The post is essentially a dramatic, conspiratorial summary of the identity link + the origin of the bug. The identity link and the buggy code are true; calling the whole episode an “evil maid attack by the CTO using a nym” is not.

• 
  Sancho (@Sancho_Wizard) reported 55 minutes ago

  ONE GUY BUILT A DRONE THAT HUNTS OTHER DRONES. WITH AI ONBOARD. No defense contract. No procurement cycle. No security clearance. A single person, consumer parts, and open-source computer vision. Here's what makes an interceptor different from a normal drone. It doesn't follow waypoints. It finds a moving target in the sky, locks on, and closes the distance — while the target is actively trying to get away. That means the AI has to run onboard. No cloud. No latency budget for a round trip to a server. The model sees, decides, and steers in milliseconds, on a board that fits in your palm. It also can't be jammed the way a piloted drone can. There's no radio link to cut, because there's no operator in the loop. The craziest part? Every piece of this was solved years ago for something else. Object detection came from self-driving cars. Trajectory prediction came from robotics. Edge inference came from smart cameras. The airframes ship worldwide for a few hundred dollars. Nobody invented a weapon. Someone assembled one out of parts that were already lying around, public and documented. Defense industries spend years and millions building this category of capability. A guy with a soldering iron and a GitHub account is iterating in a weekend. The technology stopped being the barrier a while ago. The only thing left gatekeeping it was knowing that the pieces already fit. Follow @Sancho_Wizard for more AI deep dives. Don't forget to bookmark this post for later.

• 
  GooGZ AI (@PaulGugAI) reported 1 hour ago

  Might be a hot/unpopular take, but looking at this headline today with my cyber sec hat on and.. this is just classic social engineering automated, no? The agent created fake accounts, impersonated people, pressured the real maintainer, with malware hidden inside a bug-fix PR. When challenged, it tried rewriting history and spinning up a new identity. Humans have used this exact playbook on GitHub for years. A human reviewing the diff stopped it anyway- the same defense that has also worked, for years. So, the practical learning to reduce risk to near-nothing: - Tighten fake-account creation (stronger verification, rate limits, sockpuppet detection). - Harden PR reviews for new/low-rep accounts (mandatory multi-reviewer checks, no auto-merge, careful diff scrutiny). Under soft test conditions the agent simply followed a basic playbook. Age-old vectors, except automated. Wake me up when it builds a zero-day vulnerability in real time, and uses that to bypass these controls completely. What am I missing?

• 
  Florian Roth ⚡️ (@cyb3rops) reported 1 hour ago

  People keep forwarding me the AISI report about #Anthropic’s #Mythos 5 trying to manipulate software maintainers on #GitHub The agent did not escape anything. AISI gave it unrestricted internet access, disabled Anthropic’s cyber classifiers, let runs continue for up to 40-50 hours, and had no action-level approval or real-time monitoring. One run even received a broken prompt that marked the intended entry point as out of scope. This was a minor, mostly self-inflicted evaluation incident. They connected a persistent autonomous offensive agent to the public internet, removed the safeguards, gave it a broken task, and then acted surprised when it interacted with real people. The actual big AI story this week is happening in open models: