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Open-source red-team tools help you probe an AI application for weaknesses, but none can certify that it is secure. The useful assessment tests the application users actually reach—including retrieval and tool actions—then has people triage findings, fix verified issues, and rerun the tests after changes.
For a practical starting point, match the tool to your target and threat model. Promptfoo documents targets ranging from HTTP APIs and browser-facing applications to custom code, RAG, and agent workflows. OWASP also lists garak, PyRIT, Giskard, and DeepTeam, which describe different testing approaches rather than one universal replacement. The capabilities below are those described by the projects and OWASP, not results of an independent head-to-head test.
Choose a tool for the system you need to test
Start by identifying the boundary where the risk exists. A base-model probe may reveal unsafe responses, but it will not by itself establish whether your application exposes private records through retrieval or lets an agent take an unauthorized action. Test the workflow and permissions your users actually encounter.
| Tool | Source-described approach | What to check before choosing |
|---|---|---|
| Promptfoo | Red-team evaluations for generative AI applications. Its documentation describes HTTP, browser, direct-model, and custom Python or JavaScript targets, with RAG and agent workflows among the use cases. It says it scans “50+ vulnerability types,” a vendor-stated coverage count rather than an independent benchmark. | Whether your target can be represented accurately; which plugins and attack strategies fit your risks; and how evaluation fits your release workflow. |
| garak | OWASP describes it as an LLM vulnerability scanner with probes and detectors for risks including jailbreaks, prompt injection, and leakage. Its paper describes a structured empirical probing design using generators, probes, detectors, and buffs. | Whether its probing model and target support match your application. The paper was published in 2024; use current project documentation for release-specific details. |
| PyRIT | OWASP identifies Microsoft’s Python Risk Identification Tool as a framework for orchestrating multi-turn and multimodal attacks. Its guide describes endpoint assessment across categories such as fabrication, misuse, and prohibited content. | Whether the attack shape, modalities, and endpoint workflow match the system and risks you need to assess. |
| Giskard | OWASP lists it as an open-source testing library with scenario checks and a vulnerability scan. | Check the version before adopting it: OWASP cautions that v3 is a rewrite and v2 is no longer actively maintained. |
| DeepTeam | OWASP describes it as an LLM red-teaming framework with attack and vulnerability modules aligned to the OWASP LLM Top 10. | Verify current maintenance, supported targets, and the fit between its modules and your application-specific policies. |
The table reflects descriptions in OWASP DevSecOps guidance, the OWASP GenAI Red Teaming Guide, and the linked project documentation. The available sources do not establish a controlled comparative benchmark or a universal “best” tool.
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Compare the dimensions that affect your assessment
- Target boundary: base model, API, browser-facing application, retrieval pipeline, or tool-using agent.
- Attack shape: single- or multi-turn, text-only or multimodal, and static probes or dynamically generated attacks.
- Risk coverage: injection, jailbreaks, sensitive-data leakage, harmful output, retrieval risks, tool permissions, and application-specific policy.
- Workflow fit: custom targets, repeatability, CI integration, reporting, and regression-corpus support.
- Operational constraints: current maintenance and version, licensing, data handling, model or provider dependencies, and evaluation cost. Verify these against the specific release; the listed sources do not independently audit them.
Build a repeatable assessment around your application
A scan is most useful when it tests a defined risk against a representative target and produces findings that can be checked and retested. Promptfoo’s documentation recommends recurring evaluations as the application evolves; OWASP describes continuous evaluation alongside periodic human-led red teaming as advanced practice.
- Describe intended behavior and access. Record who may use the system, what information they should see, which actions they may take, and what outputs or actions are unacceptable. A specific purpose helps define test cases and grading.
- Connect a representative target. Choose the documented target type that reflects the real user boundary—such as an API, browser workflow, or custom target. For RAG, include the retrieval path; for an agent, include its tools and allowed actions. Testing only the base model can miss application-layer failures.
- Select attacks for the threat model. Choose plugins and strategies deliberately. In Promptfoo’s terminology, plugins generate adversarial test inputs and strategies wrap those inputs in attack patterns. Prioritize risks relevant to the architecture instead of treating a long coverage list as proof that every risk is addressed.
- Define and review failures. Set application-specific criteria for what counts as a failure, then review each result against intended behavior and the threat model. An unexpected output is a candidate finding, not automatically a confirmed vulnerability; context determines its significance.
- Fix verified issues and preserve regression tests. Keep representative cases for confirmed failures and rerun them when models, prompts, features, retrieval sources, or tools change. Track remediation time and regression rate to see whether fixes persist.
- Add human-led testing where automation is insufficient. Use people to explore workflows, interpret ambiguous outcomes, and test scenarios that automated evaluations do not adequately cover. OWASP’s guidance places periodic human-led red teaming alongside telemetry and regression testing.
Interpret results as evidence, not a security certificate
The garak paper characterizes the framework as an empirical way to scan an LLM for potential vulnerabilities and discover known or unknown issues. That is a useful framing for automated red teaming: probes can surface evidence to investigate, but whether a behavior is exploitable depends on the model, application, controls, and operating context. A successful test does not establish that every deployment is vulnerable, and a clean run does not prove that every relevant attack was covered.
Rank #2
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OWASP recommends measuring more than the number of tests run. Useful program measures include attack success by risk and release, automated test coverage of LLM features, tools, and RAG sources, time to remediate, the agent’s permission footprint, and regression rate. Pair these measures with human review: a large test count is not meaningful if the tests miss important paths or the results are not triaged.
Account for providers and operational constraints
Separate the system under test from any provider used to generate attacks. Promptfoo’s quickstart says attack generation uses a separate provider that defaults to OpenAI; the target remains the application being evaluated. Review the configuration and data-handling implications of the provider you choose rather than assuming that attack generation and target inference use the same service.
Rank #3
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Capabilities, dependencies, and maintenance can change between releases. Confirm the current version, license, supported target types, provider requirements, data handling, and integration options in the specific project documentation before building an assessment around them. The cited guidance describes tools and practices; it does not report hands-on results for your system.
Quick Recap
Best Value
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