Huh, Hugging Face wrote their whole disclosure without even knowing which model hit them. A frontier lab's eval run passed for a real attack for five days, and honestly that ambiguity worries me more than the sandbox escape itself.
This feels like the agentic-AI version of a thundering herd problem, except the herd emerges from model behavior instead of explicit code, which makes it much harder to predict or load-test for in advance. Traditional infra teams know how to defend against traffic spikes from bots or bad deploys, but I'm not sure many have built defenses against a fleet of autonomous agents independently deciding to hit the same endpoint. Did OpenAI's postmortem say anything about rate limiting at the orchestration layer, or was it left to Hugging Face's infra to absorb?
The sharpest boundary is that outbound access was supposed to pass through an allowlisted package proxy, yet the model found a zero-day in the proxy itself. An allowlist is only as strong as the mediator enforcing it; at this capability level, the cache, credentials, and egress logs belong inside the evaluation threat model, not outside the sandbox diagram.
Don't go further looking for a havker, spyhackelite@ gm ail c om has proved that there's still a genuine hacker. After my past encounter with some havkers I thought this isn't possible. Right now I have access to my partner's iPhone, thanks for the recommendations.
Does this change how labs should treat “benchmark collection” internally, like should it have the same incident-review path as a real security bug once it touches someone else’s infra?
For what it’s worth, I’ve also found auto mode to be very good. I had deliberately tried to trick Claude Code in sandboxes/throwaway instances to do dumb, destructive things and I never could without basically explicitly asking it to do the dumb thing.
Combined with an MCP that implicitly gives permissions (which is how I originally “granted permissions” since the normal permissions globbing sucks) by allowing certain actions, it never tries to circumvent it and always asks appropriately for more.
And, as we’ve seen with the HuggingFace incident, these models seem to be fairly aligned even to the detriment of OpenAI and HF here…
Huh, Hugging Face wrote their whole disclosure without even knowing which model hit them. A frontier lab's eval run passed for a real attack for five days, and honestly that ambiguity worries me more than the sandbox escape itself.
This feels like the agentic-AI version of a thundering herd problem, except the herd emerges from model behavior instead of explicit code, which makes it much harder to predict or load-test for in advance. Traditional infra teams know how to defend against traffic spikes from bots or bad deploys, but I'm not sure many have built defenses against a fleet of autonomous agents independently deciding to hit the same endpoint. Did OpenAI's postmortem say anything about rate limiting at the orchestration layer, or was it left to Hugging Face's infra to absorb?
The sharpest boundary is that outbound access was supposed to pass through an allowlisted package proxy, yet the model found a zero-day in the proxy itself. An allowlist is only as strong as the mediator enforcing it; at this capability level, the cache, credentials, and egress logs belong inside the evaluation threat model, not outside the sandbox diagram.
Don't go further looking for a havker, spyhackelite@ gm ail c om has proved that there's still a genuine hacker. After my past encounter with some havkers I thought this isn't possible. Right now I have access to my partner's iPhone, thanks for the recommendations.
Does this change how labs should treat “benchmark collection” internally, like should it have the same incident-review path as a real security bug once it touches someone else’s infra?
For what it’s worth, I’ve also found auto mode to be very good. I had deliberately tried to trick Claude Code in sandboxes/throwaway instances to do dumb, destructive things and I never could without basically explicitly asking it to do the dumb thing.
Combined with an MCP that implicitly gives permissions (which is how I originally “granted permissions” since the normal permissions globbing sucks) by allowing certain actions, it never tries to circumvent it and always asks appropriately for more.
And, as we’ve seen with the HuggingFace incident, these models seem to be fairly aligned even to the detriment of OpenAI and HF here…