u/Ok_Abrocoma_6369

spent months building guardrails for our models. prompt filters, jailbreak detection, some fine-tuning on top. looked solid in testing then we ran red teaming and things started slipping through faster than expected. small variations in phrasing were enough to bypass controls that seemed reliable before.

after tightening things up, we ended up with a different problem. more false positives, legitimate queries getting blocked, and overall worse user experience. it feels like we’re trading one failure mode for another.

rn it’s not very clear what a stable setup should even look like. the more we lock things down, the less useful the system becomes. but leaving it loose obviously isn’t an option either.trying to find a balance between control and usability without constantly reacting to new bypasses.

how others adjusted their guardrails after red teaming exposed these gaps?

we have llm powered agents in prod handling customer queries and starting to see cases where behavior drifts with certain inputs. sometimes it ignores guardrails or gives answers that don’t align with what we expect.

we tried input sanitization and prompt guards, but they break on edge cases and add latency. also added output validation, but responses get rephrased in ways that slip through. did some fine tuning as well, but real user input is a lot messier than anything we trained on.

anyone else running into this. what are you using to catch behavior changes before they impact users?

open to any ideas, thanks!

we have okta sso locked down for approved apps, but people are still pasting data into random ai tools that never touch our identity stack. claude in the browser, gemini sidebars, copilot extensions, even mobile apps that don’t federate. no logs, no controls, and dlp doesn’t see it since nothing hits our normal patterns.

casb gives some saas visibility but misses browser-embedded stuff and desktop clients. endpoint agents catch a bit but don’t see browser paste events unless you go heavy on monitoring, which kills performance.

management wants visibility into ai usage across these gaps without slowing everything down or rolling out full edr everywhere.

what’s actually working for you here.. browser extensions, newer layers on top of casb, or just living with the blind spots?

we put guardrails on our internal LLM setup. rate limits, prompt filters, output checks. all fine for normal usage.

then people started pushing it.

sales began feeding contracts into prompts in ways that bypass filters. we’ve seen prompts chained across sessions to build context the model wasn’t supposed to keep. in some cases it’s generating code that reaches into data sources it shouldn’t touch.

we catch some of it in logs, but most of it looks like normal traffic. nothing obvious enough to trigger alerts.

blocking outright doesn’t really work. people just route around it using other tools or accounts. we tried browser-level controls, but performance took a hit and adoption dropped.

at this point it feels like the definition of “guardrails” breaks down once users actively test the edges.

what are you seeing when usage gets pushed like this. how are you designing guardrails that hold up under real behavior?