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garak

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What garak is

garak is a free, open-source command-line tool for probing language models and systems that use them for security weaknesses. Its probes cover issues such as hallucination, data leakage, prompt injection, misinformation, toxicity generation, and jailbreaks; detectors inspect responses for failures such as unsafe content or failure to refuse. A standard scan can run without supervision and produce a report, while scan settings can be customized. Integrations listed by the project include OpenAI, Hugging Face, Cohere, Replicate, AWS Bedrock, LiteLLM, REST-accessible models, and custom Python integrations. Hugging Face models can run locally or be reached through an inference API or private endpoint. Results include screen output, a detailed report log, a hit log, and a debug log. garak is developed in Linux and OSX, can be installed with pip or from source, and supports self-hosted deployment. Its reference documentation says it assesses text output only. The project is backed by NVIDIA and updated by NVIDIA and the community.

Who it suits

garak suits teams and developers who need to scan language models for listed security weaknesses. It supports self-hosted use and several hosted and local model access routes.

What it does well

  • Runs standard scans without supervision.
  • Supports multiple model providers and custom integrations.
  • Produces detailed report, hit, and debug logs.
  • Available as free, open-source software.

What to know first

  • Assessments cover text output only.
  • Installation is through pip or from source.
  • It is a command-line tool.

Plans

PlanBilled and limitsPrice
FreeFree tool · open-source softwareFree

As garak.ai lists them, read 7 Oct 2026. The maker's own page has the final word.

Getting started

  1. Install garak with pip or from source.
  2. Choose a supported model connection, such as a listed provider, REST endpoint, or custom Python integration.
  3. Configure and run a standard scan, adjusting scan settings as needed.
  4. Review the screen output and generated report logs.

garak is a free, open-source security scanner for language models and systems that use them. It is best suited to teams that want configurable command-line probes across hosted and local models. Its text-only assessment is a key constraint if your evaluation needs to cover other kinds of output.

Overview

garak approaches model security through adversarial probing: probes try to trigger failures, and detectors assess responses for problems such as unsafe content or failure to refuse. That makes it a focused choice for security checks rather than a broad model-evaluation dashboard.

It can run a standard scan and produce a report without supervision, while letting teams adjust scan configuration. Its stated threat coverage includes hallucination, data leakage, prompt injection, misinformation, toxicity generation, and jailbreaks. That range makes it useful for a structured first pass, though findings are limited to text output.

Key features

Integrations include OpenAI, Hugging Face, Cohere, Replicate, AWS Bedrock, LiteLLM, REST-accessible models, and custom Python integrations. Teams can apply the scanner across hosted services and locally run Hugging Face models, including models reached through an inference API or private endpoint.

Results include screen output, a detailed report log, a hit log, and a debug log. The separate outputs help teams review findings and investigate scan behavior. Support channels include a user guide, reference documentation, Discord, GitHub issues, and an NVIDIA email address. Security vulnerabilities should instead be reported through NVIDIA’s security submission form or [email protected], not GitHub.

garak is backed by NVIDIA and updated by NVIDIA and the community. It supports custom metrics and LLM-as-a-judge alongside safety evaluations, adding flexibility for teams shaping their assessment approach.

Pricing

garak is free open-source software. The Free plan costs nothing, with no free trial. That makes it a straightforward option for teams able to install and operate a command-line tool themselves; it does not present a paid tier for buyers seeking a different support or deployment arrangement.

Platforms

garak is available for Linux, macOS, and Windows, with self-hosted deployment. Developed in Linux and OSX, it can be installed with pip or from source. This suits teams that can manage a local or self-hosted workflow, but it is not a hosted web application.

Who it's for

Choose garak when you need configurable security probes across several model providers and want to run scans against local or hosted models. Its automated standard scan and multiple report outputs suit teams that need repeatable checks and follow-up detail. Look elsewhere if your assessment must include non-text outputs or you need a managed web-based service.

Pros and cons

  • Broad integration range: support for major hosted providers, REST-accessible models, and custom Python integrations helps teams use one scanner across varied environments.
  • Configurable automated scans: unattended standard scans provide a useful starting point, while configuration allows teams to tailor probing.
  • Useful report separation: detailed, hit, and debug logs give teams distinct ways to review results and troubleshoot.
  • Text-only scope: evaluations do not cover other output types, which rules it out for teams assessing multimodal behavior.
  • Command-line, self-hosted operation: installation via pip or source gives teams control, but requires them to manage the tool rather than use a hosted interface.

Alternatives

Promptfoo is worth considering if a monthly allowance of 10k red-team probes and a broader set of LLM evaluation features better fits your workflow; its Community plan is free and supports local or self-hosted runs across model providers and integrations.

Giskard is another free, open-source option for local deployment, with a basic LLM vulnerability scan using adversarial techniques and a basic RAG evaluation report.

DeepEval may suit teams seeking an open-source evaluation framework with a local and CI/CD test runner rather than garak’s security-focused scanning approach.

Arena (formerly Chatbot Arena) offers free public leaderboard comparisons and head-to-head battles, with a daily usage limit that resets after 24 hours.

Braintrust has a free Starter tier with 1 GB processed data, 10,000 scores, 14-day retention, unlimited users, projects, and datasets; it is a different fit for teams that need those evaluation and data limits.

Confident AI has a free tier capped at 2 user seats, 1 project, 5 test runs per week, and 1 GB-month of trace spans, for teams whose evaluation work fits within those limits.

Whisper is an alternative free, locally installable open-source code and model package, rather than a model-security scanner.

CloudSploit offers a self-hosted open-source version for readers looking for a different kind of security tool.

For broader discovery, see AI Red Teaming Tools, AI Security Testing Tools, and LLM Evaluation Tools.

Verdict

garak is a strong fit for teams that want free, configurable security probing across hosted and local language models, with automated scans and detailed logs. Choose it for text-focused model security checks; look elsewhere if you need non-text assessment or a hosted, graphical service.

Verdict

garak provides configurable, automated probing for language-model weaknesses across a range of integrations. Its text-only assessment scope is important for anyone evaluating broader model outputs.

Questions

How much does garak cost?

The Free plan costs nothing. The pricing model is free.

Is there a free trial?

No. The listed free option is a Free plan.

Which platforms does it support?

The listed platforms are Linux, macOS, Windows, and self-hosted deployment.

Which model services can it connect to?

Listed integrations include OpenAI, Hugging Face, Cohere, Replicate, AWS Bedrock, LiteLLM, REST-accessible models, and custom Python integrations.

What does garak assess?

It probes language models and systems that use them for security weaknesses, and assesses text output.

Who maintains garak?

The project says it is backed by NVIDIA and updated by NVIDIA and the community.

Facts from garak's own pages

Automated scans
garak can run a standard scan and produce a report without supervision, while allowing scan configuration to be customized. · docs.garak.ai, 7 Oct 2026
Deployment
garak is a command-line tool developed in Linux and OSX and can be installed with pip or from source. · github.com, 7 Oct 2026
Integrations
The project lists support for OpenAI, Hugging Face, Cohere, Replicate, AWS Bedrock, LiteLLM, REST-accessible models, and custom Python integrations. · github.com, 7 Oct 2026
Local and hosted models
The documentation describes running Hugging Face models locally or accessing them through an inference API or private endpoint. · github.com, 7 Oct 2026
Maintainer
The project website says garak is backed by NVIDIA and is updated by NVIDIA and the community. · garak.ai, 7 Oct 2026
Output scope
The reference documentation says garak supports mainstream any-to-any large models but assesses text output only. · reference.garak.ai, 7 Oct 2026
Probe and detector design
Probes attempt to elicit failures, and detectors examine model outputs for failure modes such as unsafe content or failure to refuse. · reference.garak.ai, 7 Oct 2026
Purpose
garak is an LLM vulnerability scanner that probes language models and systems using them for security weaknesses. · garak.ai, 7 Oct 2026
Reports
garak produces screen output, a detailed report log, a hit log, and a debug log. · docs.garak.ai, 7 Oct 2026
Security reporting
The repository directs security vulnerability reports to NVIDIA’s security submission form or [email protected] and says not to report them through GitHub. · github.com, 7 Oct 2026
Support
The project points users to its user guide, reference documentation, Discord, GitHub issues, and an NVIDIA email address for support. · garak.ai, 7 Oct 2026
Threat coverage
The project lists hallucination, data leakage, prompt injection, misinformation, toxicity generation, and jailbreaks among the weaknesses it probes. · github.com, 7 Oct 2026

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