Open-source red-team tools can help you find weaknesses in an AI application, but a scan is not a security certification. Test the application people actually use—including its retrieval paths and permitted agent actions—then review findings against your threat model and repeat the tests as the system changes.
What open-source AI red teaming tests
Red teaming probes an AI system with adversarial inputs to see whether it behaves in ways that violate the application’s security requirements. The important boundary is usually not just the underlying language model. It may include the user-facing application, API, prompts, retrieval pipeline, connected tools, and access controls.
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For a retrieval-augmented generation (RAG) app, that means testing the path from a user question through retrieved content to the final answer. For an agent, it also means checking what actions the system can take and whether those actions stay within the user’s authorization. Testing a base model alone cannot establish how those application-specific parts behave.
OWASP describes overlapping but distinct open-source approaches, while Promptfoo documents support for API, browser, direct-model, and custom targets, as well as RAG and agent workflows. These are source-described capabilities, not evidence that a particular deployment is secure or that one framework is best for every system. Promptfoo’s red-team quickstart · OWASP DevSecOps guidance
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How the main open-source frameworks differ
Choose a tool based on the system boundary and attack styles you need to evaluate. The descriptions below come from project and OWASP materials; they are not the result of a controlled head-to-head test.
| Tool | Documented approach | Useful fit to investigate |
|---|---|---|
| Promptfoo | Configures a target, plugins, and attack strategies; documents HTTP API, browser, direct-model, and custom targets, including RAG and agent workflows. Its official quickstart states coverage of “50+ vulnerability types,” including jailbreaks, injections, RAG poisoning, harmful or biased content, and content-filter validation. That count is the vendor’s stated coverage, not an independent benchmark. | Teams that need to configure attacks against an application boundary and run recurring evaluations. The quickstart was last updated 2026-10-07. Source |
| garak | OWASP describes it as an LLM vulnerability scanner with probes and detectors for jailbreaks, prompt injection, leakage, and related risks. A 2024 paper describes an architecture built around generators, probes, detectors, and buffs. | Teams considering structured empirical probing of model behavior. The paper explains the framework’s research context, not the capabilities of every current release. OWASP description · 2024 paper |
| PyRIT | OWASP identifies Microsoft’s Python Risk Identification Tool as a framework for orchestrating multi-turn and multimodal attacks. Its guide describes assessing endpoint robustness across categories such as fabrication, misuse, and prohibited content. | Teams whose threat model calls for multi-turn or multimodal assessment. Confirm the guide and release details that apply to your setup. OWASP DevSecOps guidance · OWASP GenAI Red Teaming Guide RC3c |
| Giskard | OWASP lists it as an open-source testing library with scenario checks and a vulnerability scan. OWASP cautions that Giskard v3 is a rewrite and v2 is no longer actively maintained. | Teams evaluating scenario-based checks should verify which version they are considering and its maintenance status before adopting it. OWASP DevSecOps guidance |
| DeepTeam | OWASP describes it as an LLM red-teaming framework with attack and vulnerability modules aligned to the OWASP LLM Top 10. | Teams looking for attack modules organized around that risk taxonomy should check the project’s current release, integration, and data-handling details. OWASP DevSecOps guidance |
There is no independent comparative score in these sources that establishes a universal winner. Before selecting a framework, check whether it can reach your real target, cover the attack shapes and risks you care about, fit your CI and reporting needs, and meet your requirements for maintenance, licensing, privacy, provider dependencies, and evaluation cost. Those operational details can change by release.
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Build a repeatable assessment
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Write down the intended behavior and threat model
Define who may use the application, what information each user should be able to access, and which actions the system is allowed to perform. Specify what counts as a security failure in this application; an output that is unacceptable in one context may be harmless in another. Promptfoo’s quickstart also recommends stating the application’s purpose so test cases and grading can be tailored to it. Promptfoo quickstart
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Connect a representative target
Choose a target that reflects the path you need to assess: an API, browser-facing app, direct model, or custom code target. For RAG and agents, exercise the user-facing workflow rather than relying only on an isolated base-model test. Confirm that the target includes the relevant prompts, retrieval sources, permissions, and tools for the assessment.
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Select attacks to match the architecture
Use plugins and strategies that address specific risks and parts of the system. Promptfoo describes plugins as adversarial generators and strategies as patterns that wrap generated inputs. For example, a RAG assessment might examine whether adversarial retrieved content can influence an answer; an agent assessment might check whether an attempted instruction can trigger an action outside the system’s intended permissions. These are test-design examples, not claims that any one tool automatically verifies the controls.
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Review each result in context
Record the input, observed behavior, target configuration, and expected behavior. Decide whether the output or action is a real failure under your threat model, and distinguish a confirmed issue from a result that needs further investigation. The authors of the 2024 garak paper describe the framework as an empirical probing tool for finding potential vulnerabilities; they also frame weaknesses as context-dependent. A probe’s result is evidence to assess, not proof that every deployment is vulnerable or safe. garak paper
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Fix the application and preserve regression cases
For confirmed findings, address the relevant control in the application, such as access boundaries, retrieval handling, or agent permissions. Keep the triggering case in a regression corpus and rerun it after the fix. Also repeat evaluations when models, prompts, features, retrieval sources, or tools change. Promptfoo recommends recurring evaluations as applications evolve. Promptfoo quickstart
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Add human-led assessment where automation is not enough
OWASP describes continuous evaluation alongside telemetry, regression testing, and periodic human-led red teaming as advanced practice. Human review is particularly useful when a finding depends on application context, a multi-step workflow, or the consequences of an agent action. OWASP DevSecOps guidance
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What to measure across releases
A scan count alone does not tell you whether risk is improving. OWASP suggests tracking measures that connect test activity to application changes and remediation:
- Attack success by risk category and release.
- Automated test coverage across LLM features, tools, and RAG sources.
- Time to remediate confirmed findings.
- The permission footprint of agents.
- Regression rate: whether previously addressed issues reappear.
Interpret these measures alongside the scope of each evaluation. A change in attack success or coverage is meaningful only if the tested target, categories, and conditions are understood.
Provider and data-flow considerations
When using Promptfoo, distinguish the target being tested from the provider used to generate attacks. Its quickstart says attack generation uses a separate provider that defaults to OpenAI, while the target is the system under test. Review the configured providers and their data handling before running tests, especially if prompts, responses, or test data are sensitive. Promptfoo quickstart
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Framework coverage descriptions tell you what a project says it can test, not how effectively it will find issues in your particular application. The listed tools have overlapping but non-identical scopes, and the sources cited here do not establish independent detection rates or comparative performance. Treat automated findings as candidates for triage, retain human review for context-dependent risks, and avoid interpreting a clean run as proof of security.
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