Comparisons anchored on garak, NVIDIA's Apache-2.0 LLM vulnerability scanner, against other open-source AI red teaming tools.
A scanner you point at a model against a framework you script attacks with: garak's probe and detector CLI from NVIDIA versus Microsoft's PyRIT with multi-turn attacks and scoring, with a verdict on which suits a one-off assessment and which suits an in-house red team.
Testing the application or testing the model: promptfoo's config-driven evals and red teaming in CI against garak's model vulnerability scans, covering coverage, reporting, OpenAI's ownership of promptfoo and how each fits a release pipeline.
The three open-source tools AI red teams shortlist: a model scanner, a scripted attack framework and a config-driven app tester compared on what each tests, reporting, CI fit and ownership, ending with which combination covers a typical LLM application assessment.
NVIDIA's probe and detector CLI against Giskard v3's scan, which builds adversarial suites from a plain-language agent description: which suits scanning a model endpoint, which suits testing an agent, and what Giskard's v3 rewrite means for teams on v2.
A command-line scanner of probes and detectors against Confident AI's Python red teaming framework with 50+ vulnerabilities, OWASP, NIST AI RMF and MITRE ATLAS mappings and multi-turn attacks, with a verdict by who runs the test and what report the buyer needs.
Tell us what you're working on, whether it's a deployment, an audit, a security test or a cyber range. You'll speak with an engineer who can help you scope it.