Armorer Guard Learning Loop: local live feedback without model drift
We just pushed the Armorer Guard Learning Loop.
It is a Rust-native hybrid live learning layer for local agent security enforcement:
- feedback adapts local enforcement immediately
- global model improvements still go through reviewed, versioned retraining
- no scanner network calls
- no silent cloud upload
- no poisoning-by-default
New CLI modes:
armorer-guard feedback-record
armorer-guard feedback-stats
armorer-guard feedback-export --reviewed-only
inspect / inspect-json now also include scan_id, model_version, and learning_version, so feedback can be tied back to a stable local scan without requiring raw text storage.
The local overlay can suppress eligible semantic false positives after feedback, or add local block/review matches. It deliberately cannot suppress credential or dangerous policy reasons like detected:credential, policy:credential_disclosure, or policy:dangerous_tool_call.
Repo/docs: https://github.com/ArmorerLabs/Armorer-Guard
Demo Space: https://huggingface.co/spaces/armorer-labs/armorer-guard-demo
Would love feedback from people building agents or eval harnesses: where would you put this loop: prompt ingress, retrieval ingress, model output, tool-call args, or all of them?
Quick release update: the Rust CLI is now live on crates.io as armorer-guard v0.2.2.
Install:
cargo install armorer-guard --locked
This release includes the local Learning Loop (feedback-record, feedback-export, feedback-stats) so teams can adapt enforcement from reviewed local feedback without uploading prompts or silently mutating model weights.
Crate: https://crates.io/crates/armorer-guard/0.2.2
Repo: https://github.com/ArmorerLabs/Armorer-Guard
Update from Armorer Labs: the classifier artifact repo has been refreshed.
What changed:
semantic_classifier_native.tsvwas updated from the latest Armorer Guard runtime artifact.- Added
semantic_classifier_profile_native.tsv, the Rust-native profile fallback used by higher-recall benchmark/strict profiles. - The model card/license metadata now reflects MIT.
- The demo Space was rebuilt against the updated artifact repo.
This is still intentionally a local, lightweight scanner lane rather than a hosted inference endpoint. The runtime combines these artifacts with deterministic credential detection, context-aware policy reasons, MCP proxy enforcement, and local feedback overlay behavior.
If you are testing agent/tool-use safety, I am interested in examples where a local scanner should warn/review rather than block outright.
Follow-up release status from Armorer Labs:
The model/artifact repo is current and now includes the refreshed native classifier TSV plus the profile fallback TSV. The GitHub runtime release v0.3.1 is also live: https://github.com/ArmorerLabs/Armorer-Guard/releases/tag/v0.3.1
Registry status is intentionally called out so people do not get confused:
- Cargo CLI:
armorer-guard 0.3.1 - PyPI wrapper: latest installable is still
0.3.0until package-token permissions are refreshed - npm wrapper: latest installable is still
0.3.0until npm login/token is refreshed
The runtime code and model metadata are MIT-aligned now.