@giliraanan@Cyberstarts1 Exactly. Once models become defenders, the bottleneck is proving they generalize beyond the lab. I’m starting to test what happens when topology, timing, and provenance shift: does the agent still contain the attack—or was it pattern-matching the benchmark?
You found that defenders often saw the implant but interpreted it as normal. Have you rerun the test with file-change history or a baseline of what normally runs on each host? I’m curious whether the models reasoned poorly, or simply lacked the context needed to recognize the change.
@theonejvo Talked about this recently with someone working in a frontier security research lab who laughed Anthropic out of the room when they talked about AI Safety.
@thomasunise Definitely worth trying. The gap I kept hitting was after the run: which agent or workflow caused a cost spike, got stuck retrying, or behaved unexpectedly? That’s actually what pushed me to build InferTrail. Curious whether you see the same once systems reach production.
@edanm@dan_lahav The question I keep coming back to: when an agent does something unexpected in a real environment, what evidence should exist afterward to distinguish model capability from an orchestration failure or credential misuse? Are teams instrumenting for that today?
@dan_lahav@Irregular One thing I kept thinking: there may be a second diffusion clock.
Weights can stay gated while capability diffuses through stolen/resold access. KYC proves who was admitted, not who is exercising it now.
That could make effective diffusion faster than open-weight diffusion.
@yacineMTB Because models are ultimately just at the core of it amazing next token predictors they can’t come up with novel designs they just copy what they’ve already seen