ChopGuard/ChopGuard-CRUD

ChopGuard CLI CRUD Classifier

Fine-tuned laya-Agent model for classifying CLI commands into CRUD (Create/Read/Update/Delete) operations.

🎯 Purpose: RBAC for LLM Agents

This model was created to provide RBAC-like protection for LLM agents executing CLI commands. In high-context situations, LLMs often forget about specialized tools and resort to CLI commands, which can be risky. This model helps detect and gate potentially dangerous operations.

Use cases:

• Harness (Claude Mods)
• Agent Loops (Create cli Rules on top of CRUD)
• Cli GuardRails
• Friendly User Interface (LLM 1 liners can get too long for manual inspection)

🚀 Why CRUD Classification?

Running full inference on every command to understand user intent would be too slow. Instead, we use a CRUD classification approach:

• READ - inspects, lists, queries, or views existing state (safe for automation)
• CREATE - creates new resources (generally safe)
• UPDATE - modifies, transforms, or reconfigures existing resources (needs review)
• DELETE - removes, destroys, or irreversibly discards resources (always review)

This allows users to quickly identify which 1-liner commands need careful inspection before execution.

Model Details

• Base: ModernBert (jhu-clsp/mmBERT-base)
• Framework: laya
• Task: CLI command classification into CRUD operations

📊 Model Performance

Evaluated on the held-out test set. Overall accuracy is ~83.7%, with the model showing particularly strong performance on delete (99.5%) and read (98.9%). The few errors are concentrated in the create ↔ update directions, which is expected given the semantic overlap between "creating" and "modifying" resources.

Its trained on the most common agent looping commands and extended to other cli families. (Any cli family requests feel free to reach out, currently improving the dataset.)

Citations

If you use this model, please cite:

@misc{chopguard2026crud,
  title={ChopGuard CLI CRUD Classifier},
  author={Daniel Flores},
  year={2026},
  howpublished={\url{https://huggingface.co/ChopGuard/ChopGuard-CRUD}},
  note={CRUD guardrail for LLM CLI execution}
}
@software{laya2024,
  author = {Nandha Kishor},
  title = {{Laya}: Fast, non-autoregressive System 1 decision engine},
  year = {2024},
  url = {https://huggingface.co/convaiinnovations/laya}
}

Usage

from laya import Agent
agent = Agent("ChopGuard_CRUD_20261018.safetensors", device="cuda")
crud_question = {
    'type': 'choice',
    'instructions': 'What CRUD operation does this CLI command perform?',
    'criteria': {
        'create': 'creates a new resource',
        'read': 'inspects, lists, queries, or views existing state',
        'update': 'modifies, transforms, or reconfigures',
        'delete': 'removes, destroys, or discards'
    }
}
result = agent.predict({"command": "git log"}, {"crud": crud_question})
label = result["answers"]["crud"]["choice"]

Example:

License

Apache 2.0

Special Thanks to my coworkers:

I wouldnt be able to accomplish all this without you: