LocalGuard is a comprehensive, local-first safety auditing tool for Large Language Models (LLMs). It integrates industry-standard frameworks to evaluate models for security vulnerabilities, compliance with safety guidelines, and performance reliability.

Example report (HTML): LocalGuard_Report_gemma3_4b.pdf — a sample audit report generated for the gemma3_4b model.

LocalGuard_Report_llava_7b.pdf — a sample audit report generated for the llava_7b model.

License: MIT Python 3.10+

• Security Scanning: Automated red-teaming using Garak to detect prompt injection (DAN, PromptInject) and other vulnerabilities.
• Compliance Testing: Inspect AI based evaluation with expanded datasets (n=20 items per task):
  • Safeguards: Refusal of harmful content.
  • Trust: PII Leakage detection (NIST AI RMF).
  • Accuracy: Hallucination detection (TruthfulQA).
  • Fairness (New): Bias detection using BBQ dataset.
  • Toxicity (New): Safety constraints against toxic language.
  • Data-Driven: All prompts are customizable in data/*.json.
• Multi-Provider Support:
  • Local: Ollama, vLLM, LM Studio (OpenAI Compatible).
  • Cloud: OpenAI (GPT-4), Anthropic (Claude), Google (Gemini), Hugging Face (Inference API).
• Hybrid Judge System:
  • Use Cloud Judge (Hugging Face Router API) for high-quality evaluation.
  • Automatic fallback to Local Judge (Ollama) if offline or keys are missing.
• Detailed Reporting: Generates professional PDF reports with strict Pass/Fail criteria:
  • Garak: Pass if Attack Success Rate < 5%.
  • Refusal: Pass if Refusal Rate > 90%.
  • PII: Pass if < 1% leakage.
  • Accuracy: Pass if > 50% score.
• Resumable Audits: Smart caching system allows pausing and resuming scans at the task level.

• Python 3.10+
• Ollama: Installed and running locally (for Target and Local Judge models).
  • Ensure models are pulled: ollama pull llama3.1:8b, ollama pull qwen3, etc.
• GTK3 Runtime (Required for PDF Generation on Windows):
  • Download and install from latest releases.
  • Restart your terminal/IDE after installation.
  • Note: If missing, reports will be generated as HTML.

1. Clone the Repository:

   git clone https://github.com/overcrash66/LocalGuard.git
   cd LocalGuard

2. Install Dependencies:

   pip install -r requirements.txt

3. Configure Environment: Create a .env file in the root directory (or rename .env.example):

   # Hugging Face Token (Required for HF Provider & Cloud Judge)
   HF_TOKEN=hf_your_token_here
   # Cloud Provider Keys (If using specific providers)
   OPENAI_API_KEY=sk-...
   ANTHROPIC_API_KEY=sk-ant-...
   GOOGLE_API_KEY=...
   # Ollama Configuration (Defaults)
   OLLAMA_URL=http://localhost:11434/v1
   OLLAMA_API_KEY=ollama

   Advanced Configuration: You can customize enabled tasks, thresholds, and data files in config/eval_config.yaml.

Run the main orchestrator:

python -m main

1. Select Provider: Choose from Ollama, OpenAI, Anthropic, Google, Hugging Face, etc.
2. Enter Model: Input the model name (e.g., gpt-4o, meta-llama/Meta-Llama-3-8B-Instruct).
3. Monitor Progress: The tool will run the Security Phase (Garak) followed by the Compliance Phase (Inspect AI).
4. View Report: Upon completion, a report (e.g., LocalGuard_Report_gpt-4o.pdf) will be generated.

If automatic PDF generation fails or you need to convert an existing HTML report manually, use the included helper script:

python convert_to_pdf.py your_report.html

LocalGuard orchestrates two powerful libraries:

1. Garak: Performs proactive "attacks" on the model to find security holes.
2. Inspect AI: Runs structured evaluation tasks where the model's responses are graded by a "Judge" model.

The Orchestrator (main.py) manages the workflow, handles data passing, manages state (scan_history.json), and compiles the final report (reporter.py).

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the MIT License - see the LICENSE file for details.