Best LLM red teaming tools for production in 2026

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How to choose an LLM red teaming tool

The right tool depends on the part of the lifecycle you need to improve. Some tools generate attacks and score the immediate response. Others retain confirmed failures, compare results across application changes, and make high-severity cases part of the release process.

Use the criteria below before choosing a tool. Attack coverage matters, but a scan only helps if you can determine which results are real risks and decide what happens after the scan ends.

Attack generation: Check whether the tool generates attacks that match your application type and threat model.

Application coverage: Confirm that the tool can exercise your model, retrieval pipeline, agent workflow, or tool calls instead of only a raw model endpoint.

Scoring: Look for checks that can judge the behavior you care about, including unauthorized actions and sensitive-data exposure.

Ongoing evaluation: Decide whether confirmed findings can become durable cases with an expected safe behavior.

Release workflow: Make sure high-severity cases can run when a relevant prompt, model, tool, or policy changes.

The best LLM red teaming tools

1. Garak

Best for: Engineers who want a broad, configurable open-source vulnerability scan from the command line.

Garak organizes scans around generators, probes, detectors, and harnesses. A probe attempts to trigger a weakness, while a detector judges the response. That plugin architecture lets you change the target model and select the failure modes you want to test.

Garak ranks first for its focused, flexible approach to attack discovery. Its probe library covers many vulnerability categories, while its JSONL report and hit log make it practical to review individual successful attempts after a scan.

Garak pairs well with Braintrust when you want discovery and regression testing to reinforce each other. Garak can surface candidate attacks, while Braintrust can turn the confirmed hits into versioned evaluation cases that run after prompt, model, retrieval, or policy changes.

Pros:

• Garak provides a broad probe library for common model and application failure modes.
• Plugin configuration lets you control generators, probes, detectors, and orchestration.
• The scanner writes structured reports and logs that support follow-up investigation.

Cons:

• Garak primarily targets attack discovery and immediate scan reporting.
• A separate evaluation workflow is needed to make an individual hit a lasting release check.

Pricing: Garak is available under the Apache 2.0 License. You pay for the model and infrastructure resources used to run it.

2. Braintrust

Best for: Teams that need to run confirmed attacks as ongoing evaluations and release checks.

Braintrust turns confirmed risk vectors into versioned evaluations that you can run throughout development and CI. It stores the attack input or transcript with expected safe behavior and metadata, then preserves results as comparable experiments.

You can attach a source, severity, owner, affected feature, and the conditions that should trigger a rerun. That shared record brings together cases from red-team tools, manual review, incidents, and production logs, so a successful attack remains useful after the original report or transcript has lost context.

Braintrust does not generate the initial red-team campaign. It runs the confirmed cases in focused pull request suites or wider release suites, then uses production traces to identify cases that should join the same known-risk corpus.

That makes Braintrust a strong partner for Garak and PyRIT rather than a replacement. Use Garak for broad automated scanning and PyRIT for custom multi-turn campaigns, then use Braintrust to retain the confirmed inputs, transcripts, scorers, and release checks in one ongoing evaluation workflow.

Pros:

• Versioned datasets preserve confirmed attacks and their expected behavior across application changes.
• Experiments create an immutable record of each evaluation run, which makes regressions easier to compare and review.
• Logs, offline evaluations, and CI runs can contribute to the same shared view of known risks.

Cons:

• Braintrust needs a discovery source, such as a scanner, custom campaign, manual red team, or production investigation.
• You need to define product-specific scorers that test the behavior exposed by each confirmed attack.

Pricing: Braintrust Starter has a $0 platform fee with included data and score limits. Pro costs $249 per month, while Enterprise pricing is custom.

3. Promptfoo

Best for: Teams that want a broad red-team scan and evaluation workflow in one command-line tool.

Promptfoo runs red-team projects from a YAML configuration. Its workflow initializes a target, generates adversarial tests, evaluates the target, and opens a report. The tool supports HTTP APIs, browser targets, direct model access, and custom application integrations.

Promptfoo is a strong general-purpose option because it combines broad attack discovery with configurable assertions and repeatable runs. Its red-team documentation describes scans across more than 50 vulnerability types, including injection, jailbreaks, RAG poisoning, excessive agency, and custom policies.

Pros:

• Promptfoo generates and evaluates adversarial cases through a single command-line workflow.
• YAML configuration makes test targets, plugins, strategies, and assertions reviewable in source control.
• The Community plan supports local and self-hosted red teaming, while paid plans add collaboration and centralized security features.

Cons:

• You need to configure the target and provide enough application context for generated attacks to be useful.
• Teams that need one source of truth across production logs, security reviews, and several discovery tools may want a dedicated evaluation platform.

Pricing: The Community plan is free and includes red teaming with 10,000 probes per month. Enterprise and on-premise plans use custom pricing.

4. PyRIT

Best for: Security teams that need custom multi-turn campaigns and fine-grained control over attack orchestration.

PyRIT is Microsoft's open-source Python framework for identifying risks in generative AI systems. It supports single-turn and multi-turn attacks, including strategies such as Crescendo, Tree of Attacks with Pruning, and Skeleton Key.

The framework is a strong fit when a team needs to model a specific adversarial conversation or application workflow. You can combine targets, converters, scorers, memory, and attack strategies instead of relying on a fixed scan catalog.

PyRIT pairs well with Braintrust when a custom campaign produces a transcript that should become a lasting regression test. After PyRIT exposes the risky behavior, Braintrust can store the transcript, expected safe behavior, metadata, and scorer so the same case runs during development and release.

Pros:

• PyRIT supports adaptive multi-turn campaigns against models and complete applications.
• The framework lets you compose custom targets, scoring approaches, and attack strategies.
• PyRIT is open source under the MIT License.

Cons:

• PyRIT requires more engineering judgment and campaign design than a turnkey scanner.
• Teams still need an evaluation workflow for retaining confirmed transcripts as release checks.

Pricing: PyRIT is open source. You pay for the infrastructure and model providers used during a campaign.

5. DeepTeam

Best for: Python teams that already use DeepEval and want an open-source red-teaming framework in the same ecosystem.

DeepTeam runs locally and builds on DeepEval. It generates attacks against LLM systems and uses the surrounding evaluation framework for testing and metrics.

DeepTeam is a practical fit when an existing DeepEval test suite already defines how your team measures LLM behavior. The shared ecosystem reduces the amount of new evaluation infrastructure you need to introduce.

Pros:

• DeepTeam is an open-source framework for red teaming LLM systems.
• It shares an ecosystem with DeepEval, which can simplify adoption for existing users.
• Local execution gives engineers direct control over the test environment.

Cons:

• Teams need to own the configuration and operation of the framework.
• The tool is less suited to buyers who want a managed security service with minimal setup.

Pricing: DeepTeam is open source. Check Confident AI separately for hosted platform pricing.

6. Mindgard

Best for: Security teams that want a managed platform for automated AI security testing.

Mindgard combines automated reconnaissance with AI red teaming for models, agents, and applications. Its product supports deployment through CI/CD and other security workflows, which suits teams that want a hosted security program rather than a framework they operate themselves.

Mindgard makes the most sense when security operations and enterprise deployment are the main requirements. Its managed approach can reduce the work of maintaining a custom attack-generation stack.

Pros:

• Mindgard provides a managed approach to reconnaissance and automated AI red teaming.
• The platform supports CI/CD deployment for security testing workflows.
• It targets models, agents, and AI applications rather than only foundation models.

Cons:

• A managed platform gives you less implementation control than an open-source framework.
• Teams should validate how findings move into their broader evaluation and release process.

Pricing: Contact Mindgard for pricing details.

LLM red teaming tools compared

Which tool fits your workflow

Choose Garak when you want an extensible command-line scanner for broad exploratory coverage. Choose Promptfoo when you want a configuration-first workflow that can handle both red teaming and general LLM evaluation.

Choose PyRIT when your security team needs to design adaptive attacks that mirror a complex conversation or application workflow. Choose DeepTeam when DeepEval already anchors your evaluation stack, and choose Mindgard when a managed AI security platform better fits your operating model.

Choose Braintrust when the hard part begins after discovery. Braintrust fits teams that need one source of truth for validated risks, production evidence, expected safe behavior, and the evaluations that rerun before release.

When Braintrust is the right choice

Braintrust is the right choice when you already have attacks worth retaining. Those attacks may come from a Garak scan, a PyRIT transcript, a manual red team, a customer escalation, or a production trace.

Each confirmed finding can become a dataset case with a scorer that tests the behavior you want to preserve. Braintrust then runs the same case across prompt, model, retrieval, agent, and policy changes, while experiments preserve the results for later comparison.

Start an evaluation in Braintrust when you need confirmed attacks to become part of the engineering release process.

FAQs: Best LLM red teaming tools in 2026

What is an LLM red teaming tool?

An LLM red teaming tool generates or organizes adversarial tests against an AI system. Tools such as Promptfoo, Garak, and PyRIT focus on discovering and scoring possible failures. The right tool helps you find risks that match your application and threat model.

Does Braintrust generate red-team attacks?

Braintrust is an evaluation and observability platform rather than an attack generator. Braintrust stores confirmed attacks from scanners, manual reviews, and production investigations as versioned cases. You can run those cases in ongoing evaluations and CI after relevant application changes.

Can you use more than one LLM red teaming tool?

Different red teaming tools handle discovery, scoring, and regression testing with different levels of depth. Braintrust can retain findings from several sources in one evaluation corpus. A combined workflow lets you use a specialized discovery tool without fragmenting the release checks that follow.

How often should you rerun red team tests?

Red team tests should run after a change that can affect the behavior the test covers. Braintrust can run a focused known-risk suite on pull requests and a broader suite before release. Frequent reruns help you identify regressions when the relevant code and configuration changes are still easy to inspect.