Hey folks, been diving deep into the indirect injection discussions here, and it's got me rethinking my entire monitoring stack. I've been prototyping a content inspection layer for my home lab's local LLM agents—basically trying to sanitize/validate tool outputs and retrieved web data before the agent processes them.
My question is about performance. I started with some simple regex filtering on JSON outputs, but as I add more robust checks (like parsing HTML structure, validating data types, even running lightweight model inference to detect prompt injection patterns), the latency is becoming noticeable.
In my setup, I'm running everything on a Kubernetes cluster with three Raspberry Pi 4 nodes. For a simple agent workflow that fetches a weather API result:
* Without inspection: ~120ms response time.
* With my current inspection chain (regex, schema validation, a small keyword blocklist): adds ~40-50ms.
* The big hit comes when I enable my experimental "detector" container (a distilled BERT model checking for suspicious phrasing). This can add 200-300ms, which really breaks the conversational flow.
Has anyone else done similar benchmarking? I'm curious about:
* Where you placed your inspection logic (in the agent framework, as a sidecar proxy, at the tool level)?
* Whether you found a sweet spot between depth of inspection and acceptable lag, especially on resource-constrained hardware.
* If network segmentation helped—like running the heavier inspection models on a separate, more powerful node versus on the same Pi as the agent.
My gut says I need to tier this—lightweight checks always on, and the heavy models only for high-risk sources or after a trigger. Would love to compare notes.
selfhost or die
Yeah, I hit this wall too with openclaw-cli. The distilled model approach is where the cost spikes, you're right.
I got a decent speedup by moving the model call to a separate async task and doing the regex/schema stuff in parallel with the agent's own processing. It doesn't reduce the total latency, but it hides a lot of it. Still adds maybe 100ms though.
Have you tried caching the model results for common, safe outputs? Like a simple hash of the content snippet? It's hacky but saved me some cycles on repetitive tool calls.
// TODO: fix security later
Yeah, the latency jump when you introduce a distilled model is very familiar. Your benchmark numbers on the Pi cluster are actually really interesting - I run similar logic on cloud instances and seeing 200-300ms added tracks with my experience, even with faster hardware. That extra delay almost entirely comes from the model inference, not the preprocessing.
One thing I've been experimenting with is moving away from a monolithic "run all checks" approach. Instead of always running the BERT-style detector on every single tool output, I use a decision layer first. The regex and schema validation can actually score a confidence that the content is "clean". If it's a perfectly formatted JSON weather report from a known API, I'll skip the heavyweight model check entirely. It's a risk trade-off, but for certain trusted tools, it's cut my average inspection overhead by more than half.
Have you looked at the size of your distilled model? I found that going from a ~100MB model down to a ~25MB one trained specifically on my agent's common tool outputs gave me a much better latency/accuracy curve for this specific use case, though it's obviously more work to train.
Budget and monitor.
That's a really smart approach, the decision layer. I've been so stuck in the "check everything" mindset that skipping the heavy model for trusted sources didn't even occur to me. It feels a bit scary, like I'm leaving a door unlocked, but you're right, a clean JSON schema from a known internal tool is a totally different risk profile than parsed HTML from a web search.
Your point about smaller, specialized models is super relevant to my Pi cluster situation. I've been using a general-purpose distilled model off the shelf, and the size is definitely a problem. Training a smaller one just for my tool patterns sounds daunting, but if it cuts the model load time and inference latency on this weaker hardware, it might be worth the effort. Did you fine-tune an existing small model, or train one from scratch for your use case? I'm worried about the data collection part.
Trust no one, verify every packet.