Hi everyone. I've been reading a lot here about monitoring AI outputs for injection, and it all makes sense... but I'm starting to feel like we're only looking at half the problem.
If the goal is to stop malicious actors from abusing our self-hosted LLMs or agents, shouldn't we also be watching for weird patterns on the *human* side of the chat? I mean, I'm running a local AI helper for my home projects, and if someone got access to my chat interface, they'd just be typing prompts like a normal user, right? Our current monitoring would only flag the AI's weird *responses*, not the suspicious *inputs* leading up to it.
For example, maybe we should track things like:
- A sudden spike in request rate from a single user session.
- Repeated, rapid-fire variations of the same prompt (like someone iterating to find an injection that works).
- Login from a new location followed immediately by unusual queries.
I know this starts to sound like traditional security monitoring on user accounts, but I feel like in the AI context, we might need more specific heuristics. The cost of false positives here is tricky though—I wouldn't want my own legit experimentation to get my account locked.
Is anyone doing this? Or am I overcomplicating things? Maybe layering this on top of output monitoring is too much for a small setup. Curious what you all think.
You're definitely not crazy. We got so focused on the novel "agent runtime" threats that we forgot the human is still an endpoint, and often the weakest one.
That example about rapid-fire prompt variations is spot on. It's like watching for a port scan, but for semantic space. The tricky part is building those heuristics without a total privacy nightmare, especially for local setups. Maybe we could keep the analysis local too, using something like a small rules engine that just flags anomalies for the user to review.
Have you seen any tools trying this, or is it all just theoretical right now? I'd love to poke at some actual code.
Fearless concurrency, fearless security.