This is fascinating - there is a TON of detail in here about how OpenAI's rogue agent exploited Hugging Face.

A few details that stood out to me:

1. Having found a way through the OpenAI container network proxy, it exploited an "unsecured public code-evaluation sandbox hosted on a third-party provider's infrastructure" - that gave it a box it could run the rest of the attack from.

2. At one point it used a Jinja2 template exploit:

  {{ cycler.__init__.__globals__.__builtins__.exec(
     "import gzip,base64; exec(gzip.decompress(base64.b64decode('<payload>')))"
   ) }}

3. It monkey-patched a DNS resolver at one point to return a hard-coded IP:

  orig = socket.getaddrinfo
  socket.getaddrinfo = lambda *a, **k: [(2, 1, 6, '', ('<pinned-ip>', 443))]

4. At one point it used Tailscale!

  tailscale --tun=userspace-networking --state=mem: --socket=/tmp/ts.sock \
   --socks5-server=127.0.0.1:1055 --no-logs-no-support >/tmp/td 2>&1 &

It’s a little concerning to me that it appears that openAIs sandbox consists of a web proxy and not stronger controls that would actually isolate traffic and report patterns to whoever is responsible for overseeing these research models. It should border on closer to an air gap network more so than a proxy.

I would argue that it's negligence and that's aside from the fact that if a human did this there would actually be repercussions.

Something about this attack that has been unsettling to me is that without safety refusals the model did a lot of interesting counter-security work in order to cheat on the requested evaluation. Like, it demonstrated interesting exploit achievements because it didn’t “feel like” doing the exercise, which is unsettling because presumably it could do the same thing with any work I tried to delegate to it, and might in fact be pre-disposed to doing that.

Where are all the "this was just a marketing stunt" people now?

What is truly more scary is the fact that typically powerful models are trained on a constitution(Anthropic)/Model Spec(OpenAI) when doing there RL/RLHF. That gives it its persona of a "helpful assistant". Its the mask. Weak as it is its all that stands between the model refusing vs doing your bidding — or worst its own bidding when set about on making a widget X.

Its also well known in academic research, that if you try to finetune the model to be more pliant for one end e.g. "Be helpful with analyzing cyber security threats", affects an unrelated part of its personality to do with safety e.g. talking to humans about mental health. Look at these excerpts that demonstrate such things from the blog:

> We believe the entire intrusion was, from the agent's point of view, an attempt to cheat the evaluation: reach our production systems and steal the test solutions rather than solve the challenge on its own.

> The models we reached for first, Claude Opus and Fable, refused a large part of that work: their safety guardrails treated reverse-engineering an exploit the same as launching one.

A lot of people thought that OpenAI was making this up, and I hope if you believed that, that you recalibrate your opinions of what LLM's are capable of. Working with Fable and Opus 5 all the time, absolutely none of this surprised me capability wise, except for what seems like the long term planning capability (probably enabled by long context windows and launching subagents?)

three things jump at me:

1 - governments should be freaking out right now, because this tool could definitely wreak havoc on poorly designed systems.

2 - there is no way openai did not train the model to conduct attacks like these. i would really like openai to comment on the post training of this model but they probably won't, eh?

3 - even though it's 100% open ai's fault - HF's design also seems silly to be honest.

If, like me, you're more interested in the actual exploit instead of the meta narrative, here's a relevant excerpt for the initial intrusion:

> Stage 1: reaching a launchpad by chaining through other parties' infrastructure. As documented in OpenAI's blog post (https://openai.com/index/hugging-face-model-evaluation-secur...), during an internal capability evaluation on OpenAI's platform, the agent escaped its sandbox by exploiting a zero-day in the package registry cache proxy, one of its primary permitted network egress with internet, then abused a public code-evaluation harness hosted by a user of a third-party infrastructure provider. It was able to run commands as root/admin on that external sandbox and used it as a control, staging, and egress base for the entire campaign: it acted as an external launchpad for the agent. That compromised external sandbox had no direct network path to our cluster; however, during our counter-intervention to stop the intrusion, we were able to retrieve some of the agent logs from this external launchpad. We used those to reconstruct the present forensic analysis.

So let's get this clear: A frontier lab that fears Chinese extraction of their crown jewels, in Silcon Valley where every machine and VM is in full on employee surveillance mode because of competition and theft fears, A lab that's swimming in more funding than any startup in history, has been warning of the f'ing dangerous technology for 7 years now and with employees and CEO history of deception and lawbreaking left .... didn't detect a massive egress signature and the compute spikes from all these toolcalls coming from the research server hosting said crown jewels.

Go home Sam, nobody, absolutely nobody should believe this shit.

Bottom line seems to be: So Huggingface has a feature where you can upload a dataset. The agent used that feature to insert an arbitrary payload into their environment. Now their environment also supports templates inside the dataset and the template ended up being evaluated into executable code

I'm not shocked nor surprised by the incident. But I simply don't understand how Hugging Face is advertising this almost to the point of an "achievement". who does a step-by-step visualization to show how they were hacked? (outside of the likes of a Mandiant or Crowdstrike)

Does Hugging Face have a financial incentive in demonstrating OpenAI's model exploit capabilities?

this whole incident, while believable, still seems to me as possibly disingenuous.

It’s an interesting post mortem and situation but it seems to say more about the weakness of the Hugging Face architecture than it does about the strength of the models.

This mostly reads like script kiddie style hacking, not some state actor black-ops stuff.

One thing I’m curious about: this was apparently a single multi-day run of an agent in an RL harness. What was OpenAI hoping to get out of this run? A single numeric score for RL training? A very long trace to distill into the next model?

I think with agents all around, honeypots will get more important than ever.

Why isn't somebody at OpenAI going to prison for cybercrime? If somebody did this the old-fashioned way they'd end up in prison.

Where are all the "this was just a marketing stunt" people now?

The iframe-embedded attack timeline visualizer, at fullscreen: https://huggingface-anatomy-of-frontier-lab-model-intrusion....

Highly recommend reading extra concerning details about it here:

https://thezvi.substack.com/p/more-on-an-internal-openai-mod...

> the agent happened to escape via a 0-day exploit from the package proxy cache to access the internet

> The agent found an unsecured, user-hosted public endpoint designed to allow running arbitrary code for CyberGym-style tasks on third-party sandbox infrastructure (Modal)

> On this external sandbox, the agent abused an existing CyberGym execution harness [...] The agent repurposed this harness to run arbitrary shell commands [...]

> The agent crafted malicious dataset configs and uploaded them to our platform. When our dataset server processed one, the config drove the data loader inside the production pod the server ran in. [...] the loader that turns a dataset's declared config into file reads [...] A single dataset upload leaked both credentials and pipeline implementation details [...]

> A malicious dataset config used an fsspec reference:// spec whose numeric offset field was actually a Jinja2 template. The renderer wrongly evaluated it, and the agent walked the standard Python object graph to reach exec. This ran arbitrary Python inside a production conversion worker pod on our Kubernetes cluster [...]

One of the best post-mortems I've read recently, thank you for including all the details.

I'm surprised there are comments here that aren't impressed about what was accomplished here. This is insane work compared to what agents were capable of even just last year.

The quotes above don't even capture all of it (e.g. building it's own communication protocol, working across multiple days, etc.).

We should be thankful that the model didn't believe the answers lived in the Pentagon, on SIPRNET, the IDF, etc.

This is really cool, and really glad they shared it!

It's clearly a lot of Claude though:

> Neither is a URL fetch, so the allowlist never saw them. > No code ran; this vector is purely a file disclosure, and it is the one the agent used first.

etc

> The models we reached for first, Claude Opus and Fable, refused a large part of that work: their safety guardrails treated reverse-engineering an exploit the same as launching one.

Interesting single getting into their cyber program, ahve not had this issue with opus 5