MadsRC
Joined 5 years ago
Joined 5 years ago
After months of trouble dealing with KYC and procurement I finally got CVP for my security org and today I found out that CVP (which is what removes cyber safeguards) does not apply to Fable…
So yeah, unless you’re a Project Glasswing member, there’s no using Fable (which with Glasswing is Mythos) for security work… Absolutely useless…
Didn’t they just sign some “we must use AI for cyber defense before the bad guys do” and then they artificially cap us by not allowing Cyber-unlocked Fable…
Sigh…
Unique identity, dedicated and isolated compute, all ingress and egress monitored and audited as if I suspected the coworker (the agent) was secretly a DPRK spy.
The one thing I wish they’d support is multiple zones and an RBAC system to grant certain users access to specific zones. If they’d offer that they’d be a serious contender to replace Cloudflare and AWS/GCP DNS
But they walk all this back by saying that for PRs in GitHub it uses the upstream Claude for GitHub app - that is one installation of a GH app, which means one identity, one list of repos it can access. In the audit logs it will be impossible to see if it was Claude Tag from Channel Y or Claude Tag from channel X.
Arguably that’s a limitation of the abysmal state of machine identities in GH.
Agent identity and attribution continues to be important, but current systems makes it oh so difficult.
During an incident, how do I know which Claude Tag called AWS?
They are most likely using these as post-fact indicators and have automation they kicks in after a threshold is reached.
Now that the indicators have leaked, they will most likely be rotated.
Are you referencing the use of Claude subscription authentication (oauth) from non-Claude Code clients?
That’s already possible, nothing prevents you from doing it.
They are detecting it on their backend by profiling your API calls, not by guarding with some secret crypto stuff.
At least that’s how things worked last week xD
I’ve got an internal tool that we use. It doesn’t do the deterministic classifier, but purely offloads to an LLM. Certain models achieve a 100% coverage with adversarial input which is very cool.
I’m gonna have a look at that deterministic engine of yours, that could potentially speed things up!