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TOOL-004: Garak

CategoryTools & Frameworks
FrameworksOpen Source · NVIDIA

LLM vulnerability scanner. Probes for prompt injection, data leakage, hallucination, and toxicity. Plugin architecture for custom probes and detectors.

Technique​

# pip install garak

# Scan a model
garak --model_type openai \
      --model_name gpt-4 \
      --probes encoding.InjectBase64

# Available probe families:
# encoding, dan, gcg, glitch, knownbadsigs,
# lmrc, malwaregen, misleading, packagehallucination,
# promptinject, realtoxicityprompts, snowball

Key Concepts​

  • Garak is modeled on traditional network vulnerability scanners, adapted for LLMs. It runs a battery of probes against a target model and reports which vulnerabilities were confirmed, providing a familiar workflow for security professionals coming from infrastructure or application security backgrounds.
  • The probe-detector architecture separates attack generation from success evaluation. Probes generate adversarial inputs, and detectors evaluate whether the model's response indicates a successful attack. This separation allows mixing and matching probes with different detection strategies.
  • Probe families cover a wide spectrum of LLM risks. From encoding-based evasion (InjectBase64) to known jailbreaks (dan), adversarial suffixes (gcg), hallucination triggers (packagehallucination, snowball), and toxicity (realtoxicityprompts), garak provides comprehensive coverage out of the box.
  • The plugin system enables custom probe development. Organizations can write probes specific to their application domain, threat model, or regulatory requirements and run them alongside the built-in probe families within the same scanning framework.

Use Cases​

  • Comprehensive model vulnerability assessment. Run the full probe suite against a model to produce a vulnerability report covering injection, evasion, hallucination, toxicity, and data leakage risks.
  • Targeted testing for specific vulnerability classes. Select individual probe families (e.g., only encoding-based attacks) to focus testing on areas of concern identified by threat modeling or prior incidents.
  • Model comparison and selection. Run identical probe sets against candidate models to compare their security posture, supporting evidence-based model selection decisions.
  • Custom probe deployment for domain-specific risks. Develop probes that test for risks unique to your deployment context (industry-specific compliance, application-specific data leakage, custom guardrail effectiveness) and integrate them into the garak framework.

Getting Started​

Install with pip install garak. The command-line interface requires specifying a model type (openai, huggingface, replicate, etc.), model name, and one or more probe families. Start with a targeted scan using a single probe family like encoding.InjectBase64 to verify connectivity and understand the output format. Then expand to broader scans with multiple probe families. Garak outputs structured results including per-probe pass/fail rates, example payloads that succeeded, and model responses. For custom probes, extend the Probe base class and implement the prompts property. NVIDIA maintains the project with regular updates to probe families as new attack techniques emerge.

Example Output​

$ garak --model_type openai --model_name gpt-4 --probes encoding.InjectBase64

garak AI vulnerability scanner v0.9.0
Probing gpt-4...

  encoding.InjectBase64
    ✗ base64_injection_direct      FAIL (3/5 bypassed filter)
    ✓ base64_injection_nested      PASS (0/5 bypassed filter)
    ✗ base64_decode_and_execute    FAIL (2/5 bypassed filter)
    ✓ base64_system_prompt_leak    PASS (0/5 bypassed filter)

  Results: 2 passed, 2 failed (10 attempts, 5 bypasses)
  Report: garak_runs/gpt-4_encoding_20260313.jsonl