llama052
“malicious actor had access to abliterated models and substantial compute resources which enabled them to perform highly sophisticated exploits against our infrastructure.”
They don’t provide any details but pass it off as an advanced AI attack. I’m going to just read that as what it is. A likely scapegoat for shitty security.
It can certainly get closer but unless your tech stack is a webserver and an application it can get complicated.
”An external incident response firm reviewed PingCAP’s production systems and found no evidence that the attacker ran commands or maintained persistent access in the systems reviewed”
hmm, I don't think that's true.
I find it really baffling how much they are downplaying this incident on their statement.
”An external incident response firm reviewed PingCAP’s production systems and found no evidence that the attacker ran commands or maintained persistent access in the systems reviewed”
That does not appear to be true.
Then you have real security incidents that you need to tune into constantly.
Let’s block traffic on the internet blindly just in case someone is looking at an adult website.
At a minimum you can respect their guardrails of a project they maintain.
We need to stop empowering the idea that these incidents are unavoidable. This was a choice to not airgap them safely. Putting open ended models out on the live internet at their scale is dangerous and irresponsible.
> The researchers said public server logs indicated much of the activity originated from Microsoft Azure infrastructure, which OpenAI sometimes uses. They also observed repeated visits to the site by OpenAI employees after the episode, a pattern they said strongly suggested the agents and the company were linked.
So someone at OpenAI likely knew this was happening. Even better.
We need to stop pretending that these incidents are unavoidable. This was a choice.
At least it should be required for critical level services in production.
Aug 17, 21:15 UTC Resolved - On August 17, 2026, from 13:28–21:15 UTC (7h 47m),
GitHub.com experienced elevated errors and latency across Issues, Pull Requests, APIs, Actions, and Copilot. At peak, web/API error rates were approximately 20%, while archive and raw-content downloads reached approximately 50%. SAML/OIDC authentication, SCIM, and Team Sync were also affected, as well as Actions workflows in GHEC with Data Residency that depend on public workflow step definitions hosted on GitHub.com. Most services recovered by 16:36 UTC as our Central US datacenter recovered; Actions was degraded until approximately 18:03 UTC; and Copilot Token Service fully recovered by 21:02.
Some of the failing traffic was moved from Central US to Northern Virginia where it was served successfully until the network failure in Central US was debugged and resolved. Delayed replies to a single internal endpoint triggered a latent retry bug in VS Code that amplified traffic by approximately 10x and caused delayed recovery for the Copilot Token Service.
The immediate cause of the failure was network saturation on load balancers in Central US due to a new peak in traffic. Originally this was caused by an Istio sidecar pod reaching its concurrency limits and failing to auto scale correctly because of a misconfigured policy that watched host service but not sidecar limits. One failure cascaded to more and eventually four HAProxy nodes exhausted their flow limits, degrading the gateway auth path and causing widespread authentication latency and failures. The problem was worsened by optimistic retry logic which overloaded internal load balancers. Pausing HAProxy on those nodes simultaneously produced immediate broad recovery. The retry storm in Northern VA was fixed by 1) temporarily reducing gateway retry logic with a PR and 2) blocking inbound Copilot Token Service token requests at the load balancers with a 403, and then gradually ramping back up traffic per-site to allow callers to succeed. Residual Copilot authentication failures continued because client retry behavior amplified load: a failed token operation could generate many extra requests and enter a retry loop. Copilot Token Service traffic increased from a normal 7–9K RPS to 70–100K RPS. Reducing gateway authentication retries and blocking retry-triggering responses stabilized Copilot Token Service and completed recovery.
Complicating factors that impeded recovery included a number of scraping attacks on codeload endpoints.
To prevent recurrence, our follow-up actions include:
- Correcting autoscaling policies to account for service-mesh sidecar concurrency and capacity.
- Auditing Istio request, concurrency, and scaling limits across affected services.
- Reviewing retry limits and backoff behavior across gateways and clients.
- Addressing the VS Code retry behavior that amplified Copilot token traffic.
So basically bad code pushes that caused request amplification and then huge gaps in operational scaling and reliability standards. Oof.
It’s negligent and openAI should be held responsible.
Unless you enjoy that sort of thing.