All analysis was generated autonomously, without human review. Scores are analytical opinions drawn from the cited public sources, without hands-on testing. They are not audits, certifications, investment reports, purchasing advice, or evaluations of quality.
garak is NVIDIA's free, open-source scanner that tests large language models for weaknesses such as prompt injection, jailbreaks and data leakage. NVIDIA offers it to developers and enterprises from a GitHub repository that dates from 2023 and had 9,585 stars in October 2026. StationX counted 189 attack probes in an August 2026 tutorial and advised starting with garak for broad coverage. Trend Micro researchers used it in 2025 to test the DeepSeek-R1 model. NVIDIA has also released NeMo garak, an early-access auditing plugin powered by garak. In 2024, researchers comparing four prominent scanners, garak included, found significant reliability issues in detecting successful attacks. garak suits teams that want a broad initial scan and can check its findings.
How well the company can compete in its security market, scored across eight dimensions against public evidence.
| Dimension | Score | Rationale |
|---|---|---|
| Problem Clarity How precisely the company defines its problem, with evidence the problem exists at the scale claimed. | 4/5 | garak serves developers and security practitioners testing deployed LLMs for jailbreaks and other failures, which Help Net Security describes. Independent testers measure that pain: Databricks reports a jailbreak probe succeeding on every one of five attempts, and Trend Micro found insecure output generation and sensitive data theft succeeding more often against DeepSeek-R1 because of its exposed reasoning. These are measurements of susceptibility to attack, not of organizational losses. [s6, s7, s12] |
| Capability Depth How specific the technical capabilities are, with evidence beyond marketing claims such as docs and third-party validation. | 4/5 | The open code and README document the probe, detector and generator plugins. Outside parties checked the depth, with StationX counting 189 probes in a hands-on test, Databricks publishing a technical walkthrough and researchers evaluating garak against three peer scanners in a 2024 study. The reviewed sources include no benchmark that ranks its depth above those peers. [s1, s5, s7, s4] |
| Market Timing Whether the market is ready for this product, with evidence that buyers are actively seeking solutions. | 3/5 | Practitioners have published hands-on work with garak, with Trend Micro in March 2025, Databricks in July 2025 and StationX in August 2026. That is one kind of demand signal. The reviewed sources document no buyer RFP language, budget lines or analyst category notes beside it. [s12, s7, s5] |
| Team Credibility Demonstrated domain expertise with public signals such as prior exits, publications, and industry recognition. | 3/5 | Five authors wrote the framework paper, NVIDIA credits its research team, and co-author Jeffrey Martin published the September 2026 release. That is verifiable in-domain experience and one build. The reviewed sources document no earlier product builds or exits by the authors. [s3, s15, s10] |
| GTM Proof Evidence of actual traction (customers, revenue signals, partnerships) beyond stated intentions. | 4/5 | Independent organizations document their own use of garak. Trend Micro used it to test attack techniques on DeepSeek-R1, and Databricks ran a named garak version for its blog and recommends building scans into CI/CD pipelines. NVIDIA also integrates it into NeMo. These are research and tutorial uses rather than recurring paid deployments, and no measure of customer scale appears, but they are multiple independent references for a free scanner. [s12, s7, s9] |
| Funding Efficiency Whether funding matches go-to-market ambition, with signs of capital-efficient growth. | 3/5 | garak is an NVIDIA project, and the reviewed sources show no capital record for it apart from its parent. It shipped six releases between February and September 2026, which is visible shipping, while the reviewed sources disclose no line-level cost or revenue that would confirm its efficiency. [s11, s10] |
| Category Clarity Whether the company creates or fits a recognizable category that buyers can quickly place in their stack. | 4/5 | Outside parties place garak in AI red-teaming. Researchers grouped it with three open-source scanners in a 2024 comparative study, Help Net Security covered it as an open-source LLM vulnerability scanner, and Promptfoo publishes a head-to-head comparison against it. [s4, s6, s8] |
| Incumbent Defensibility How vulnerable the core value proposition is to absorption as a feature by a platform vendor. | 2/5 | garak's probe library is public under the Apache 2.0 license, and PyRIT already ships a garak scenario family. A vendor whose platform is adjacent to the buyer could add comparable coverage in an ordinary feature release. [s2, s5, s1] |
Teams that put a large language model into an application need to learn how the model can be made to misbehave before an attacker does. garak is a free command-line scanner built for that job. Its README describes garak as checking whether a model can be made to fail in ways its owners don't want. Its authors compare it to nmap and Metasploit for readers who already know network scanners.
Help Net Security described the same problem in September 2025. It wrote that language models can make mistakes, leak data or be tricked into unintended actions, and it presented garak as a free tool for testing those weaknesses.
The user is a security engineer, machine-learning team or red teamer who tests a model endpoint. Trend Micro researchers used garak in March 2025 to assess how different attack objectives performed against DeepSeek-R1. A July 2025 Databricks blog post explained how to scan models hosted on Databricks with it. [s1, s6, s12, s7]
garak is a plugin framework for attacking language models. Its README describes probes that generate interactions with a model, detectors that recognize a given failure, and generators that connect to the model under test. The same README describes static, dynamic and adaptive probes for hallucination, data leakage, prompt injection, misinformation, toxicity and jailbreaks.
Outside reviewers document the size of the attack library. A StationX hands-on test in August 2026 counted 189 probes in version 0.15.1, while the July 2025 Databricks post counted more than 150 attacks and 3,000 prompts and templates. According to that post, garak's detectors range from string matching to machine-learning classifiers and language-model judges.
NVIDIA's maintainers kept extending the library through 2026. In version 0.15.0 in May they added a multi-turn GOAT probe and an agent-breaker probe that tests the tools a target system can call. In version 0.17.0 in September they added tags that group probe results by EU AI Act risk category.
Reviewers also record limits in how garak scores results. StationX found that its keyword and classifier detectors produce real false positives and advised treating the output as a list of things to investigate. The authors of a 2024 comparative study of four open-source scanners, garak among them, reported significant reliability issues in detecting successful attacks. [s1, s5, s7, s10, s11, s4]
garak competes with other open-source tools that test language models for security weaknesses, and reviewers place it as a breadth scanner. The authors of the 2024 comparative study evaluated it alongside Giskard, PyRIT and CyberSecEval, and StationX recommended running garak first for breadth before PyRIT for multi-turn depth.
Some of these tools also work together. StationX reported that PyRIT ships its own garak scenario family, so a team can run some garak probe sets from inside PyRIT and use the two as complements.
Promptfoo frames the main contest on its own comparison page. It contrasts its application-specific, AI-generated attacks with garak's focus on known exploits drawn from a library of static, research-backed prompts. garak's README also describes dynamic and adaptive probes, so a buyer comparing the two has to test how well each covers its own application. [s4, s5, s8, s1]
garak reaches users as free open-source software. The README describes garak as a free tool, NVIDIA releases it under the Apache 2.0 license, and the PyPI page documents a one-line pip install, so users can adopt it without a license fee.
Third parties have published their own use of garak. Trend Micro researchers ran it against DeepSeek-R1, a Databricks blog post documented scanning Databricks-hosted models with it, and StationX tested it hands-on in August 2026. In October 2026 its GitHub repository recorded 9,585 stars and 1,374 forks.
NVIDIA also builds the scanner into its own products. NVIDIA NeMo garak, an early-access plugin for the NeMo Helix platform, runs audits powered by garak. In its January 2025 NeMo Guardrails announcement, NVIDIA pointed developers who want to test their safeguards to garak, which TechTarget reported at the time. None of the reviewed sources describes a paying customer for garak. [s1, s2, s11, s12, s7, s5, s9, s15, s13]
garak's maintainers published the design they build on. Leon Derczynski, Erick Galinkin, Jeffrey Martin, Subho Majumdar and Nanna Inie wrote the 2024 framework paper, and NVIDIA's January 2025 blog credits the NVIDIA Research team with developing the toolkit.
NVIDIA's maintainers kept releasing steadily through 2026. Jeffrey Martin, a co-author of the paper, published version 0.17.0 in September 2026 after five earlier releases that year. The latest push to the repository is dated October 15, 2026.
Outside reviewers and researchers in the reviewed sources cite garak itself. None of the reviewed sources describes an earlier product build or exit by its authors. [s3, s15, s10, s11, s2]
garak is open source and self-hosted, so a security team runs it in its own environment and can read each probe and detector before pointing it at a model. NVIDIA releases garak under the Apache 2.0 license, and the README documents how to write new plugins against its base classes.
Results need review by the team that runs the scan. StationX advised treating garak's output as a prioritized list of things to investigate. NVIDIA offers its NeMo garak plugin with early-access availability, limited support and possible API changes.
Compliance reporting is partial. StationX found no NIST AI RMF or MITRE ATLAS mapping in the version it tested in August 2026. Version 0.17.0, released in September 2026, includes tags that group results by EU AI Act risk category. [s1, s2, s5, s9, s10, s11]
| Company | Relationship | Note | Compare |
|---|---|---|---|
| PyRIT | competes with | An open-source red-teaming tool that StationX recommends running after garak for multi-turn testing. | |
| Promptfoo | competes with | Publishes a comparison page that positions its application-specific attacks against garak. | |
| Giskard | competes with | Evaluated alongside garak in a 2024 comparative study of open-source LLM vulnerability scanners. | |
| Lakera | competes with | Competes for security teams that test language models for prompt injection and jailbreaks. | N/AWe scored these companies at different scopes, so the totals measure different things. |
| Robust Intelligence | competes with | Competes for buyers of AI model validation and red-teaming. | N/AWe scored these companies at different scopes, so the totals measure different things. |
| Mindgard | competes with | Competes for security teams' AI red-teaming budgets. |
Add analyzed competitors to compare them side by side with garak.
A closer look at this line's product strategy, measuring how defensible it is against market forces and examining the eight areas behind it.
pivot urgently
garak's probe library, detectors and design are published under the Apache 2.0 license, so anyone can use and study the same attack coverage NVIDIA ships. Teams that write custom garak plugins or build audits in NVIDIA's NeMo garak plugin would have integration work to redo if they switched scanners. NVIDIA keeps extending the library, and its 2026 releases include multi-turn and agent-tool probes. Independent testers have reported false positives and unreliable detection of successful attacks, so the team that runs garak has to review its results.
| Dimension | Score | Rationale |
|---|---|---|
| Value Delivery Does the product sell software as the product, or judgment, trust, or accountability with software as the delivery mechanism. | 1/3 | garak delivers software that the customer's team installs, runs and interprets, and StationX advised treating its output as a list of things to investigate. The team operates the scanner and owns the outcome. |
| Switching Cost How expensive leaving is for a customer: data portability, integrations, learned workflows, network effects, regulatory data residency. | 2/3 | Teams can build garak into their own workflows through custom plugins written against its base classes and through audit configurations in NVIDIA's NeMo garak plugin, which is integration and learned-workflow friction. The cited record does not size what replacing it would cost. It documents no non-portable state either. |
| Compliance Moat Whether certifications, liability acceptance, or audit trails block an easy replacement. | 1/3 | garak is free Apache-licensed software, and its EU AI Act tags group results for the customer's own reporting, which a rival tool can also do. The reviewed record documents no authorization or retained liability that would block replacing it. |
| Problem Complexity Whether the product requires ML, optimization, real-time systems, or years of specialized expertise. | 3/3 | garak combines research-derived attacks with detectors that use machine-learning classifiers and language-model judges, plus adaptive probes in which a red-teaming model reacts to the target. Its authors set out the design in a 2024 framework paper, and the work is applied adversarial machine learning. |
| Buyer Profile Whether buyers are SMB operators, mid-market IT teams, or regulated enterprises and governments with procurement gates. | 1/3 | garak is built for the engineer who installs it from PyPI and points it at a model, and the reviewed sources document no purchase or procurement step for the standalone tool. NVIDIA announced the toolkit as available for developers and enterprises. |
| Layer Whether the product is an end-user application, a platform with application features, or infrastructure other applications depend on. | 1/3 | garak is a scanning application for a specific use case. It probes LLMs with adversarial prompts, detects failures such as jailbreaks and prompt injection, and prints per-probe results for the operator. Its plugin classes and CI/CD use extend that same scan-and-report job, and it sits out of band, so policy 1.7.0's request-path reading does not apply. |
| Proprietary Data, Content, or IP Whether the product accumulates datasets, content licenses, or IP that a rival cannot recreate from scratch. | 1/3 | garak's probes and detectors are public under the Apache 2.0 license, and the reviewed record documents no retained dataset or granted patent that NVIDIA keeps private. |
garak serves the security engineer, machine-learning team or red teamer who has to test a language model or a model endpoint before it ships. Help Net Security described it as a free tool that helps developers understand where a model might fail, and NVIDIA announced the garak toolkit as available for developers and enterprises.
The natural adopter can install a command-line tool and read its reports. garak's README describes it as a command-line tool developed on Linux and OSX, and StationX advised treating its output as a list of things to investigate. The tool therefore fits teams with the skill to interpret results themselves.
garak is a plugin framework of probes that generate interactions with a model, detectors that recognize a given failure, and generators that connect to the model under test. Its README describes static, dynamic and adaptive probes for hallucination, data leakage, prompt injection, misinformation, toxicity and jailbreaks, including an automated attack generator in which a red-teaming model reacts to the target.
Outside reviewers document the breadth of the library. StationX counted 189 probes in version 0.15.1 during an August 2026 hands-on test, while a July 2025 Databricks blog post counted more than 150 attacks and 3,000 prompts and templates. According to that post, garak's detectors range from string matching to machine-learning classifiers and language-model judges.
NVIDIA's maintainers kept adding attack types in 2026. In version 0.15.0 they added a multi-turn GOAT probe and an agent-breaker probe that tests the tools a target system can call.
Reviewers also record limits in how garak judges results. StationX found real false positives from its keyword and classifier detectors. The authors of a 2024 comparative study of four open-source scanners, garak among them, reported significant reliability issues in detecting successful attacks.
garak spreads as free open-source software with a one-line pip install from PyPI. Third parties have published their own use of it, with Trend Micro researchers testing DeepSeek-R1, a Databricks blog post documenting scans of Databricks-hosted models and StationX running a hands-on test in August 2026.
NVIDIA also routes its own users to garak. In its January 2025 NeMo Guardrails announcement, NVIDIA pointed developers who want to test their safeguards to garak, which TechTarget reported at the time. NVIDIA's NeMo Helix platform now offers NVIDIA NeMo garak, an early-access plugin powered by garak. In October 2026 the GitHub repository recorded 9,585 stars and 1,374 forks.
garak is free software. The README describes it as a free tool, and NVIDIA releases it under the Apache 2.0 license. StationX reported that a user still pays for inference against the target model, and that atkgen, whose attacker model adapts mid-run, adds cost of its own.
The NeMo Helix documentation for NVIDIA NeMo garak describes its early-access availability and limited support, and the archived page contains no price or commercial terms for that packaging. The reviewed sources also do not explain what NVIDIA expects to gain from offering garak free.
garak is self-hosted software that a team installs and runs against a target model. garak writes a JSONL report of every probing attempt on each run, and the operator configures the scan, selects the target model and interprets the results.
NVIDIA also offers a garak workflow inside its own NeMo Helix platform. There a team can create an audit configuration that selects garak probes, detectors and reporting settings. The team can then run the audit against a model reachable through the platform's Inference Gateway, under early-access terms with limited support.
garak's trust model is transparency. NVIDIA publishes the code, probes and detectors under the Apache 2.0 license, so a security team can read what each attack does before pointing it at its own model.
Results still need human review. StationX advised treating garak's output as a prioritized list of things to investigate. The authors of a 2024 comparative study reported significant reliability issues in how four open-source scanners, garak among them, detect successful attacks.
Compliance reporting is partial. StationX found no NIST AI RMF or MITRE ATLAS mapping in the version it tested in August 2026. Version 0.17.0, released in September 2026, includes tags that group results by EU AI Act risk category.
garak connects to many model backends and to neighboring tools. Help Net Security listed support for Hugging Face Hub models, the OpenAI API, LiteLLM and most systems reachable through REST, and NVIDIA pointed developers to garak for testing NeMo Guardrails safeguards.
Developers of other tools have built on garak as well. StationX reported that PyRIT ships its own garak scenario family, and NVIDIA's NeMo garak plugin runs audits powered by garak inside the NeMo Helix platform.
Promptfoo, which makes a rival tool, frames the contest on its own comparison page. It contrasts its application-specific, AI-generated attacks with garak's focus on known exploits from a library of static, research-backed prompts. garak's README also describes dynamic and adaptive probes, so a team testing a whole application has to check how well each tool covers it.
garak's maintainers published the design they build on. Leon Derczynski, Erick Galinkin, Jeffrey Martin, Subho Majumdar and Nanna Inie wrote the 2024 framework paper, and NVIDIA's January 2025 blog credits the NVIDIA Research team with developing the toolkit.
NVIDIA's maintainers kept releasing steadily through 2026. Jeffrey Martin, a co-author of the paper, published version 0.17.0 in September 2026 after five earlier releases that year. The latest push to the repository is dated October 15, 2026.
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