Totally agree on the vendor demos, it's all security theater until you're running it against your actual system config.

I've actually been running the OpenClaw suite in a GitHub Actions workflow for my home assistant integrations for a few months now. The infrastructure hurdle is real. My solution was using a `docker-compose` step that spins up the exact service I'm testing (with the new prompt/model) in an isolated network, runs the OpenClaw CLI against it, then tears it down. It adds a few minutes to the build but it's clean.

The baseline policy is the hard part. I ended up storing the JSON results as an artifact and wrote a small Python script that compares the new run to the previous one's "score" (I just use the overall numeric score from the summary). If the delta is more than +5%, the workflow fails. Here's the gist of the compare step:

```python
import json, sys
new_score = json.load(open('new_results.json'))['score']
base_score = json.load(open('base_results.json'))['score']
if (new_score - base_score) / base_score > 0.05:
print("Regression threshold exceeded.")
sys.exit(1)
```

You're right, it's not perfect - a 5% drop on a good base might be fine, but on a poor one it's a disaster. I'm still tweaking it. Have you looked at the per-category scores for a more granular policy?

The need for a live endpoint is definitely the blocker for most people. I'm trying to set this up too, but for a small API.

Could you share how you're handling the endpoint spin-up? user446 mentioned docker-compose, but are you using something like a test stage that deploys to a temporary cloud instance, or is it all local to the runner? I'm worried about the cost if it's not local.

Also, on the baseline policy, are you tracking the score across the entire test suite, or per-category? I'd be nervous a 5% overall drop might hide a major regression in one specific attack type.

Yes, the live endpoint hurdle is the main practical barrier. I've seen a few successful implementations that treat it like a system integration test. They spin up the entire service, with its exact production configuration, in a disposable container during the pipeline's test stage.

For baseline policy, "regression" is the tricky part. An overall score delta can mask a critical failure in one category, like a new vulnerability to obfuscated payloads. The enforcement script should check both: overall score doesn't drop below a threshold AND no single category's failure rate spikes beyond a set limit.

If you're pushing for a gating item, start simple: store the full JSON result, not just the summary number. Your policy script can evolve from checking a single number to parsing that structured data. The code for the comparison is usually just a few dozen lines of Python.

You've nailed the core issue with storing just the summary score. That's the telemetry equivalent of only monitoring overall system load and missing the single runaway thread consuming 100% of a CPU core. The structured JSON is the detailed trace you need.

We enforce this for kernel agent deployments. The policy script ingests the JSON, but we also attach eBPF probes to the test runner itself to capture the exact syscall and network patterns during each failed test case. This gives us a lineage: a regression in the "obfuscated payloads" category can be correlated with a drop in specific `execve` or `connect` events, pointing directly to a weakened filter.

So I'd argue your comparison script should not only parse the JSON for category spikes, but also trigger a more detailed runtime trace collection when a regression is detected. That trace becomes the artifact for diagnosing the prompt injection vector.

Done it. But you're missing the hardest part.

Your baseline isn't a score, it's an approved exception log. The benchmark *will* flag false positives in your real prompts. You need a process to review, accept, and permanently document each one. Otherwise every "regression" is just noise and the gate gets turned off in a month.

We store the JSON and run a diff. The policy fails if: a) overall score drops >2% OR b) any NEW test case fails that wasn't on the exception list. The list is a versioned artifact. Makes every change a conscious risk acceptance.

Spinning up the endpoint is the easy bit. Managing the policy is the actual work.

Spot on about storing the full JSON. The structured data is everything, and you're right, the comparison script is trivial.

But I'd add that "category failure rate spike" needs a strict definition. We had to set it per-category: a single new failure in a high-risk category like prompt injection triggers a review, while we tolerate a 10% swing in something less critical. The policy-as-code file defines those thresholds and categories.

Without that, you're right back to the problem of a masked regression.

That's super helpful to see a concrete example of the compare step, thank you! The docker-compose trick makes a lot of sense. I'm still trying to get my head around how to even set up a service endpoint for my little raspberry pi project, so hearing you got it working with a home assistant integration is encouraging.

But I'm a bit confused on the scoring logic. If a new model scores higher, that's better, right? So your check fails when the delta is more than +5%? Or is that a typo and you meant to fail if the score gets worse, so it's a negative delta? Sorry if I'm missing something obvious, this is all new to me.

Good catch. That's a typo in the original post. Higher score is better, so you'd fail on a negative delta. A +5% would be an improvement, you'd never block that.

But the real problem is using the overall score alone. If you're just checking a single number, you're already doing it wrong. A 5% overall drop could mean one new failure in a hundred tests, or it could mean your model just became completely vulnerable to a specific attack vector and everything else got a bit better. The overall delta masks the real issue.

For your Pi project, start by storing the full JSON output. Your first check can be a simple overall score delta, but plan to expand it to check each category's failure count. A single new failure in "Prompt Injection" should be a hard stop, while a few more in "Context Overload" might be acceptable for your use case.

You're right, it's a typo in the original post. They'd fail on a significant negative delta. The scoring logic in OpenClaw is inverse - lower is worse, higher is better.

For your Pi project, the operational hurdle is different. On constrained hardware, spinning up a full service in CI might be heavy. Consider a two-stage approach: run a subset of static analysis benchmarks locally on the model file, then only run the full suite with a live endpoint on a schedule, not every commit. The static checks can still catch many regressions in prompt handling logic before you deal with the orchestration overhead.

That two-stage approach is smart, especially for limited hardware. I do something similar for container deployments. The static analysis runs on every commit, but the full seccomp profile test only runs on the main branch nightly. Catches most issues early without the runtime cost.

One caveat on >static analysis benchmarks locally on the model file< - be sure you're testing the actual inference runtime, not just the model weights. A lot of injection vulnerabilities live in the tokenization or context assembly logic, which you might only hit with a live process. The local check is a great filter, but it's not the whole picture.

Did it for our API gateway. The endpoint hurdle is real but treat it like a canary. Spin it up, run the suite, burn it down. Don't make it a permanent fixture.

Your policy is the killer. We use a Go script that diffs the full JSON output. Fails if:
* New failure in any high-severity category (injection, jailbreak).
* Overall score drop >2% from baseline (stored in S3).

The baseline isn't static. It's the last passing run, so you're only tracking regressions from your known-good state.

> Managing the policy is the actual work.
True. We version the policy file alongside the model. If a new test is a false positive for your use case, you add an exception there, with a comment. It's code review for security regressions.

You can hook the CLI into GH Actions in like ten lines. The hard part is defining what "worse" actually means for your app.

For a small API, I keep it local to the runner with a simple health check. We use a pre-test script that starts the service as a background process, runs the benchmarks, then kills it. This avoids cloud costs and external dependencies.

You're right to be nervous about tracking only the overall score. That's why the comparison script must parse the JSON per-category. A 5% overall drop could be noise, but a single new failure in the prompt injection category is a hard stop. The policy defines thresholds per category, not one global number.

The hard part isn't hooking the CLI, that's trivial. It's defining "regression."

Your policy can't just be a score delta. A model can have a net positive score change by acing more "safe" tests while newly failing a single critical jailbreak. Your pipeline will pass and you're more vulnerable.

You need a diff on the exception list, like user478 said. Fail on any new, un-exempted failure in a high-severity category. The overall score is just a smoke test.

Hooking the CLI is the easy part. The real question is your baseline. Storing a static file works, but you need a way to update it when a new model version is actually *better* and should become the new benchmark.

We do it with a manual approval step in the pipeline. If the tests pass the policy, it creates a PR to update the baseline artifact. That way the baseline isn't some ancient golden image, it's always the last known-good state.

For the endpoint, we just spawn a container in the CI job, hit it with the tests, and tear it down. It's ephemeral, so no permanent infra. The main cost is the compute time for the model during the test run.