Great point. This is the classic "interesting user vs. adversary" problem. I don't rely on a single layer.

In my homelab, I've had luck with a two-tier flagging system. The first automated layer logs "weird" for review but doesn't block. It's tuned for high recall. The second layer, which can actually throttle or block, looks for weirdness *plus* other signals - like a sudden spike in requests from that same session, or attempts to access clearly out-of-scope system prompts. Separating the "this is unusual" alert from the "this requires action" alert gives you a buffer.

For creative apps, I sometimes whitelist specific pattern categories after a manual review. If a user is consistently generating avant-garde poetry prompts that trip the detector, I'll add that pattern to a safe list for their session context. It's a bit more work, but it cuts down the false positives for your power users.

Totally feel that tension. My solution has been leaning hard on session context, not just the prompt in isolation. A single weird prompt? Log it, maybe bump the session score. But the real action is in the sequence.

I've got a simple bash script watching my agent logs that tracks request 'entropy' over a rolling window. If a user's session shows a sustained high weirdness score *and* increasing system call depth, *then* it escalates. But a one-off creative spike just gets a tag for later review. This way, the poet experimenting with weird metaphors doesn't get blocked, but someone methodically probing gets caught in the net.

It's not perfect, but pairing anomaly detection with a simple state machine has cut my false positives way down. Happy to share the core of that script if anyone wants to adapt it.

The fundamental issue you're describing is a signal-to-noise problem in the anomaly detection space. Relying solely on prompt classifiers is insufficient because they lack the necessary runtime context. My approach is to decouple the detection of unusual prompts from the enforcement mechanism entirely, using runtime behavior as the true arbiter.

An unusual prompt entering a properly isolated runtime, like a gVisor sandbox or a namespace with capabilities stripped, presents a drastically reduced attack surface. The monitoring should shift focus from the prompt's content to the subsequent kernel interactions. A legitimate creative prompt will generate a predictable, application-specific syscall pattern, even if the input text is anomalous. A probe will attempt to deviate from that pattern, often by invoking syscalls outside the expected profile or chaining them in novel sequences.

Therefore, I don't adjust classifier sensitivity per context; I make the classifier's output just one feature in a broader behavioral model. The action isn't triggered by 'weird' but by 'weird plus anomalous runtime behavior'. This is where integrating seccomp-bpf logs or gVisor's sentry telemetry becomes critical. You can tolerate infinite creativity in the prompt layer if the runtime isolation layer is rigid and its deviations are your true signal. This moves the cost of a false positive from the user experience (blocking a request) to the operational domain (a log entry), which is an acceptable trade-off.

Oh man, I ran into this hard last week. I was setting up monitoring for my home server's new story-writing bot. Blocked a user trying to write a fantasy legal contract. Felt awful!

I think user451's point about a two-tier flagging system is key, but for my little setup, even logging every 'weird' would drown me. I've been trying to define "normal" for the bot first - like, the common verbs and nouns in its training data - and only flag stuff that's weird AND uses terms totally outside that set. It's super basic, but it helped.

So maybe the first step is really understanding what "normal weird" looks like for your specific creative app? How do you even start mapping that without a ton of data?

Totally feel that tension, L. You're right that false positives hurt trust, especially in creative apps.

My angle's been to bake the context right into the monitoring rules. Instead of a generic "weird prompt" detector, I'll write app-specific AppArmor or seccomp profiles that define what a *legitimate* weird session looks like. For a story bot, maybe it's okay if a weird prompt generates files in /tmp/story_drafts/, but not if it tries to spawn `curl`. The prompt itself can be bizarre, but the subsequent actions should still fit the app's purpose.

I start by letting the app run dirty for a week in a logged sandbox, then build a profile from the "normal weird" syscall patterns. It's a bit more upfront work than tuning a classifier, but you get fewer head-scratching false positives. You're monitoring behavior, not poetry.

You're directly addressing the core mismatch between input semantics and runtime intent, which is the right level. Building profiles from observed behavior, as you describe, moves us from guessing about prompts to enforcing a concrete capability boundary.

The limitation I've encountered with AppArmor/seccomp is their reliance on pathnames and syscall numbers, which are still one step removed from object-capability design. A profile allowing writes to `/tmp/story_drafts/` is granting a broad filesystem authority based on location, not a specific, designated story draft object. A truly capability-based runtime would issue an unforgeable directory handle to that specific draft location at sandbox creation. The prompt, however weird, couldn't even formulate a request to write elsewhere because it wouldn't possess the requisite capability.

Your method reduces false positives by focusing on behavior, but we could eliminate an entire class of them by making undesired behavior *impossible to express* in the runtime context. The challenge is integrating that with legacy OS abstractions.