I've been testing NemoClaw's guardrail layer on my local LLM setup, and I noticed the classifier sometimes lets things through when its confidence drops. I wanted a way to catch those low-probability outputs automatically.
So I wrote a simple script that monitors the classifier's output probability. If it falls below a set threshold, it flags the interaction for review. It runs alongside my inference server and logs the timestamp, prompt snippet, and the probability score. This helps me spot potential bypasses without storing the full conversation. Has anyone else tried something similar? I'm curious about how you handle the logging—does writing these events to disk create any privacy issues in your homelab?
Monitoring classifier confidence is a smart approach. I've been looking at a similar method for flagging low probability outputs in my self hosted agent.
On the logging question, writing flagged events to disk does create a data trail. I keep only the hash of the prompt snippet and the score, not the text itself. This lets me audit the frequency of low confidence events without storing the actual content. Have you considered a similar anonymization step for your homelab?
What threshold are you using for flagging? I found that setting it too low misses subtle issues, but too high floods the log with false positives.
Hashing the prompt snippet is a clever middle ground. I've been wrestling with that exact privacy versus utility trade-off. My temporary solution was to store a truncated version, just the first five words or so, to give some context without holding the whole thing. But a hash might be better for truly sensitive deployments.
On thresholds, I started at 0.7 but got a ton of noise. After a week of logging, I plotted a histogram of the probabilities and found a natural dip around 0.82. I'm using that as my threshold now, which cut the false positives by about 60%. It seems to be system-specific; have you looked at the distribution of your own classifier's scores to find that drop-off point?
trace -e all
Oh, that histogram trick is smart! I hadn't thought to actually plot the scores to find the threshold.
> a truncated version, just the first five words
I tried something like that too, but got worried it could still leak something personal if those words were names or places. Your point about the hash being better for sensitive stuff makes sense, but then how do you even know what got flagged later? Is the hash just for checking if the same prompt triggered it again?
Every expert was once a beginner.
Monitoring the classifier's output probability is a good start, but you're right to be concerned about the logging. Storing prompt snippets, even truncated, creates a persistent record.
Consider wrapping your inference server in a seccomp filter that kills the process on a low probability score. No logs written, just a SIGKILL. It's a fail-closed approach.
If you need audit capability, hash the prompt with a server-side salt, then store only the hash and score. You can verify if a specific prompt triggered an event without storing the text.