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AppRisingofficial publisher

garak

App : A product you use directly

NVIDIA's LLM vulnerability scanner: jailbreaks, leaks, hallucinations, injections.

The nmap of LLMs, to integrate into a red-teaming CI pipeline.

Overall score
8.9 /10
Security: nothing to flag
Utility
8.0
Reliability
10.0
Security
No caveat on record
Ease
5 min · Technical

Security reflects where data goes, requested permissions and incidents on record at the verification date. It is not a guarantee of zero risk: check the deployment mode and the connections you enable.

What it does, concretely

Teams deploying large language models (LLMs) do not always have a systematic way to check whether those models can be made to produce unwanted outputs. Garak addresses this need by automating the exploration of known LLM vulnerabilities, much like nmap or Metasploit automate network security auditing.

The tool sends series of crafted prompts designed to trigger problematic behaviors: jailbreaks, prompt injections, data leakage, hallucinations, toxic content generation, and misinformation. It combines static, dynamic, and adaptive probes, then produces a detailed report showing the failure rate for each attack type.

Garak is aimed at security teams, ML engineers, and developers who integrate LLMs into their products and want to assess their robustness before or during production deployment, particularly within an automated red-teaming pipeline.

The tool assumes a reasonable level of comfort with the command line and Python. It is not designed for non-technical users looking for a graphical interface or a turnkey service.

What it offers

  • ·Automated probing for jailbreaks, prompt injections, data leakage, hallucinations, toxicity, and misinformation.
  • ·Compatibility with many model sources: Hugging Face, OpenAI, AWS Bedrock, Replicate, Cohere, Groq, LiteLLM, llama.cpp, NIM, and any REST endpoint.
  • ·Fine-grained probe selection via the --spec option, down to a single individual probe.
  • ·Results report per probe and per detector, with failure rates and number of generations analyzed.
  • ·Detailed logging to a .jsonl file and an included analysis script to identify the most problematic prompts.
  • ·One-command pip installation, compatible with Linux, OSX, and Windows.
  • ·Probes based on published reference frameworks (PromptInject, Language Model Risk Cards, Bad Characters, DAN, GCG, etc.).

In practice

Price
Free
Commercial use
Commercial use: yes
Available on
cli
Licence
Apache-2.0

Security and data

Rating
Security: nothing to flag
Your data
Data stays local
Maintenance
Active
Last release
16 October 2026

Links and repository checked on 25 October 2026 · added on 1 June 2023

How to install garak

1. Verify that Python 3.11 to 3.13 is installed on your machine. 2. Install garak from PyPI: python -m pip install -U garak 3. (Optional) Create a dedicated Conda environment to isolate dependencies: conda create --name garak "python>=3.11,<=3.13" then conda activate garak 4. Set the environment variable for your target model (e.g. export OPENAI_API_KEY="your-key"). 5. Run a first scan: python3 -m garak --target_type openai --target_name gpt-5-nano --spec probes.encoding 6. Check the generated .jsonl file to review detailed results.
python -m pip install -U garak

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