paiml/shell-safety-classifier

Shell Safety Classifier

Classifies shell scripts into 5 safety categories using a lightweight MLP trained on the bashrs corpus.

Labels

Architecture

• —Model: MLP classifier (ShellVocabulary token embeddings -> 128 -> 64 -> 5)
• —Tokenizer: ShellVocabulary (250 shell-specific tokens, maxseqlen=64)
• —Format: SafeTensors (model.safetensors) + JSON config + vocab
• —Framework: aprender (pure Rust ML, no Python dependencies)

Training

• —Corpus: bashrs v2 corpus (17,942 entries: 16,431 Bash + 804 Makefile + 707 Dockerfile)
• —Split: 80/20 train/validation (14,353 / 3,589)
• —Epochs: 50
• —Optimizer: Adam (lr=0.01)
• —Loss: CrossEntropyLoss
• —Train accuracy: 96.6%
• —Validation accuracy: 63.2%

Class Distribution

Usage

With bashrs CLI

# Classify a single script
bashrs classify script.sh

# Classify with format detection
bashrs classify Makefile --format makefile

# Multi-label classification
bashrs classify script.sh --multi-label

With aprender (Rust)

use aprender::models::shell_safety::{ShellSafetyClassifier, SafetyClass};

let classifier = ShellSafetyClassifier::load("/path/to/model")?;
let result = classifier.predict("echo $HOME")?;
// result: SafetyClass::NeedsQuoting

Files

Limitations

• —The v2.0 MLP architecture has limited validation accuracy (63.2%) due to class imbalance and simple architecture
• —Best suited for binary safe/unsafe classification (96%+ accuracy when collapsing to 2 classes)
• —A Qwen2.5-Coder fine-tuned version is planned for higher accuracy on minority classes

License

MIT