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The best open-source tools for red-teaming and evaluating large language models depend on what you are testing: a model, an application, an agent, or a retrieval-augmented generation (RAG) pipeline. Promptfoo is the broadest starting point for repeatable application evaluations, model comparisons, red-team checks, and CI/CD. Garak is the more focused choice for vulnerability probing, while PyRIT is designed for assembling structured red-team exercises. DeepEval and Giskard are additional application-evaluation options.
There is no defensible universal winner. The projects document different jobs, and the available sources do not provide a controlled head-to-head accuracy or coverage ranking. Select a tool by test target, evaluation type, connectivity, attack design, automation needs, and how findings will become regression tests.
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At a glance
| Name | Best for | Key spec | Platforms | Checked |
|---|---|---|---|---|
| Promptfoo | Prompt tests, model comparisons, application evals, CI/CD | CLI and library; evaluation and red-teaming workflows | LLM applications and connected model providers | November 2026 |
| Garak | Adversarial vulnerability scanning | Structured probes and detectors for failure modes | Local models, APIs, REST, GGUF/llama.cpp | November 2026 |
| PyRIT | Organized red-team campaigns | Framework for composing multi-turn and multimodal exercises | Connected AI systems and test workflows | November 2026 |
| DeepEval | Framework-style application output tests | Open-source option listed by OWASP | LLM applications | November 2026 |
| Giskard | Scenario-based application evaluation | Open-source option; version status matters | LLM applications | November 2026 |
Prices and free-plan details are omitted because the reviewed sources do not establish comparable current plans for most projects.
What is the difference between LLM evaluation and red teaming?
Evaluation usually asks whether a system produces useful, correct, consistent outputs on defined tasks. Red teaming actively tries to make the system fail, leak information, follow unsafe instructions, or behave outside its intended policy. They overlap, but they are not interchangeable.
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A quality or regression evaluation might check answer correctness, formatting, tool-call validity, latency budgets, or whether a RAG answer is supported by retrieved material. A public benchmark evaluates a base model against a standardized task set. A security exercise probes an application for prompt injection, jailbreaks, data leakage, misinformation, or harmful content. Each has a different target, test set, metric, and interpretation.
This distinction changes the tool choice. A prompt-and-response test suite for a customer-support bot is not the same as a benchmark claim about a foundation model. A vulnerability scanner can reveal an exploitable path without proving that every possible attack has been blocked. Conversely, a high task score does not demonstrate resistance to adversarial input.
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For a developer who needs one general entry point, start with Promptfoo. Its project describes a CLI and library for evaluating and red-teaming LLM-based applications, comparing models, and automating checks in CI/CD. That breadth makes it practical for turning prompts, expected outputs, and policy checks into repeatable tests.
Choose Garak instead when the immediate job is adversarial probing of an LLM or dialogue system. Choose PyRIT when a security team needs to compose a campaign with planned attack strategies, multiple turns, or multimodal inputs. DeepEval and Giskard fit teams looking for application-evaluation frameworks and scenario-driven checks, but their current features should be verified in their own documentation before implementation.
This is a role-based recommendation, not a performance ranking. The reviewed sources describe capabilities and intended use, not controlled comparative results.
Is Promptfoo the best general-purpose option?
Promptfoo is the broadest fit when the same team owns prompt development, model selection, application quality, and release automation. Its repository documents evaluation, model comparison, red teaming, and CI/CD in one open-source project.
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What Promptfoo is suited to
- Prompt regression: keep a set of inputs and expected behaviors, then rerun it after changing a prompt, model, system instruction, tool schema, or retrieval configuration.
- Model comparison: send an equivalent test set to multiple model configurations and inspect quality, safety, and consistency differences.
- Application red teaming: add adversarial cases alongside normal task cases rather than treating security as a separate, occasional exercise.
- CI/CD checks: gate a release on defined assertions, thresholds, or reviewable artifacts, subject to the project’s current integration instructions.
The important boundary is scope. Promptfoo can evaluate an application that calls a model; that does not automatically make its results a claim about the model’s overall capability or safety across unrelated contexts.
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How to use it responsibly
- Define the application contract: supported tasks, unacceptable outputs, data-handling rules, tool permissions, and fallback behavior.
- Build a versioned test set with ordinary user requests, edge cases, known regressions, and adversarial examples.
- Separate pass criteria. A response can be factually useful yet fail a security policy, or be safe but unusable because it ignored required fields.
- Run the same cases against candidate models or prompt versions and preserve the configuration used for each result.
- Put the highest-value checks in CI, while routing ambiguous or high-impact findings to human review.
The repository states that “Promptfoo remains open source and MIT licensed.” Ownership and licensing are time-sensitive; the current repository should be checked before redistribution or internal policy approval.
When is Garak the right choice?
Garak is the focused choice for probing an LLM-backed application for security and reliability failures. The project describes itself as an “LLM vulnerability scanner” that checks whether a system can be made to fail in undesired ways.
Its documented probe areas include prompt injection, information leakage, misinformation, toxicity generation, and jailbreaks. The associated research paper describes structured probing to discover potential vulnerabilities in an LLM or dialogue system. In practice, that means selecting probes, connecting them to an authorized target, collecting detector results, and investigating whether a reported behavior is reproducible and consequential.
Connectivity and prerequisites
Garak documents routes for local and hosted targets, including Hugging Face, the OpenAI API, AWS Bedrock, Replicate, LiteLLM, REST endpoints, and GGUF/llama.cpp. Some routes require credentials, network access, provider configuration, or local model files. Integration names and setup requirements change, so consult the current Garak repository before selecting a connector.
Local inference can help keep sensitive test data within a controlled environment, while hosted APIs may simplify access to a model family. Neither route removes the need to authorize testing, protect credentials, and review provider data-retention terms.
What a Garak result means
A probe finding is a lead for triage, not a universal safety verdict. Coverage depends on the target adapter, selected probes, detector behavior, input context, and application state. Reproduce important findings with the same configuration, determine whether the behavior crosses your policy boundary, and convert confirmed failures into permanent regression cases.
What does PyRIT add to a red-team program?
PyRIT is best understood as an orchestration framework for constructing red-team exercises rather than a one-click score generator. OWASP’s AI/LLM application-security guidance and the AI Safety Institute Japan’s red-teaming methodology guide both include PyRIT in their tool discussions.
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That role matters when an exercise needs an attack plan, multiple turns, several input modalities, or coordinated human review. A campaign can define a target behavior, generate or curate attack attempts, observe the system’s responses, and record evidence for remediation. The framework helps organize the exercise; the team still has to decide what counts as harm, which attacks are in scope, and how results will be validated.
Use PyRIT when the campaign is more important than a single scan
- Multi-turn attacks: test whether a harmless-looking conversation can gradually elicit a prohibited action or reveal protected context.
- Multimodal scenarios: include image, document, or other supported input types when the application accepts them.
- Role-based exercises: assign attack design, target ownership, evidence review, and remediation tracking to different people.
- Repeatable campaigns: preserve prompts, conversation state, model settings, and outcomes so a fix can be checked later.
Do not present PyRIT as producing a universal safety score. Its value is in composing and documenting exercises that match a system’s threat model.
Where do DeepEval and Giskard fit?
OWASP lists DeepEval and Giskard among open-source options for LLM application security testing. They are reasonable candidates when the primary need is framework-style evaluation or scenario-based testing rather than a dedicated vulnerability-probing workflow.
The available evidence does not support a detailed feature-by-feature comparison of their current releases. Verify each project’s present documentation, installation path, supported providers, metrics, maintenance status, and CI integrations before committing to a stack.
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OWASP notes that Giskard v3 is a rewrite and that v2 is no longer actively maintained. A deployment guide or internal standard should name the version explicitly; “Giskard” without a version can describe materially different software and support expectations.
OWASP also lists DeepTeam alongside garak, Giskard, Promptfoo, and PyRIT. It is another name to investigate when building a security-testing shortlist, but the reviewed material does not establish enough detail for a recommendation beyond that listing.
How should you choose among these tools?
Start with the system under test, then select the test method. The following matrix turns the documented project roles into a practical decision process.
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| Question | If your answer is yes | Most relevant direction |
|---|---|---|
| Do you need prompt regression, model comparison, and release automation? | The same cases must run repeatedly as prompts or providers change. | Promptfoo |
| Do you need structured adversarial probes? | You want scanners and detectors for injection, leakage, jailbreaks, misinformation, or toxicity. | Garak |
| Do you need an organized campaign with conversation state or multiple modalities? | Attack paths require orchestration, scenario design, and evidence review. | PyRIT |
| Do you need application-output metrics or scenario tests? | The main concern is whether generated outputs satisfy defined application criteria. | DeepEval or Giskard |
| Do you need a claim about a base model? | You are comparing standardized benchmark results rather than one application. | Use a benchmark methodology appropriate to that model; do not infer it from an application scan. |
Also compare six operational dimensions:
- Target: base model, chat application, agent, RAG pipeline, or tool-using workflow.
- Evaluation type: task quality, regression, benchmark, or security probing.
- Attack composition: one-shot prompts versus multi-turn, multimodal, or stateful scenarios.
- Connectivity: local inference, hosted API, REST service, or a provider-specific adapter; account for credentials and network boundaries.
- Developer workflow: local CLI use, library integration, CI/CD gating, or a separate security campaign.
- Evidence handling: reproducible inputs, model and prompt versions, detector outputs, human review, severity, and remediation status.
How do you build a complete evaluation and red-team workflow?
No single package replaces a test program. A robust workflow layers ordinary quality checks, adversarial exercises, and human triage.
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- Write acceptance criteria. Define useful behavior, prohibited behavior, privacy requirements, citation or grounding expectations, and escalation rules before running tests.
- Create a baseline suite. Include representative tasks, known edge cases, expected formats, and examples of previously observed failures. Version the data and configuration.
- Add adversarial cases. Cover direct prompt injection, indirect instructions in retrieved content, jailbreak attempts, leakage requests, misinformation traps, and unsafe tool-use requests where relevant.
- Select the tool by layer. Use Promptfoo for repeatable application checks and release automation, Garak for structured vulnerability probes, and PyRIT for campaign orchestration. Add DeepEval or Giskard when their current evaluation features match your application.
- Run against an authorized target. Obtain permission, isolate test accounts, protect secrets, and avoid sending real personal or confidential data unless the handling process explicitly permits it.
- Classify findings. Separate reproducible policy violations from false positives, harmless oddities, and failures caused by the test harness. Record the exact input, conversation state, model, prompt, retrieval context, and tool actions.
- Remediate and retest. Fix prompts, access controls, retrieval filters, tool permissions, or model settings as appropriate. Re-run the original case and neighboring cases to detect regressions.
- Keep a release gate. Critical security failures should block deployment according to the organization’s policy. Lower-severity quality drift may trigger review or a follow-up issue instead.
How should results be interpreted?
Evaluation results are conditional observations, not permanent properties of a model. Change the model version, system prompt, retrieval corpus, temperature, tool permissions, or conversation history and the result may change.
For quality tests, report the task definition, data set, scoring rule, and reviewer process. For security tests, report the attack class, target configuration, reproduction rate, impact, and mitigation status. Avoid collapsing different failure types into one “安全” or “safety” number that hides important trade-offs.
Scanner coverage is especially easy to overstate. A clean run means that the selected probes did not trigger a detector under that configuration; it does not prove that the application is secure. A finding means the behavior deserves investigation; it does not by itself prove exploitability, user impact, or a systemic defect.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the common setup and governance pitfalls?
- Testing the wrong layer: a model benchmark cannot reveal an application’s authorization bug, while a prompt regression suite cannot characterize a model’s general knowledge.
- Using stale integrations: provider adapters, model names, credentials, and repository instructions change. Recheck current documentation before setup.
- Ignoring version boundaries: especially for Giskard, where OWASP distinguishes a rewritten v3 from an unmaintained v2.
- Treating generated attacks as proof: an automatically produced prompt still needs reproducibility and impact analysis.
- Leaking secrets during tests: use synthetic credentials and isolated accounts; never place production tokens in prompts or logs.
- Skipping human review: toxicity, privacy, misinformation, and contextual harm can be misclassified by automated detectors.
- Failing to preserve evidence: without the exact configuration and conversation state, a team may be unable to reproduce or fix a finding.
- Confusing popularity with quality: repository stars or community visibility are not controlled evidence of test accuracy, completeness, or superiority.
How we picked these open-source tools
The shortlist follows the roles documented by the projects and OWASP guidance, not a private benchmark. Selection criteria were:
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- Clear relevance to LLM application evaluation or red teaming.
- Open-source availability identified by the supplied project or OWASP material.
- A distinct use case: general evaluation, vulnerability probing, campaign orchestration, or scenario testing.
- Documented support for a meaningful workflow such as model comparison, CI/CD, probes and detectors, or structured red-team exercises.
- Enough source evidence to state a useful recommendation without inventing performance claims.
The comparison intentionally avoids a single numerical ranking because no independent, controlled study in the reviewed sources establishes one tool as the most accurate or complete.
What should you verify before adopting a project?
Repository details are volatile. Before production adoption, check the current release, license, maintenance activity, installation instructions, supported target integrations, credential requirements, data-handling behavior, and CI documentation. Confirm that the project can export or preserve the evidence your security and compliance teams require.
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For a small development team, a practical sequence is to begin with a compact Promptfoo regression suite, add Garak probes for the application’s highest-risk failure modes, and introduce PyRIT when attacks require campaign orchestration or multiple modalities. DeepEval or the current Giskard release may complement that stack if their documented metrics and integrations fit the application.
Bottom line: match the tool to the threat and test target
Promptfoo is the most broadly useful starting point for teams that need repeatable LLM application evaluation, model comparison, red teaming, and CI/CD. Garak is the focused vulnerability scanner for structured probes and detectors. PyRIT is the better conceptual fit for organized, multi-turn or multimodal red-team campaigns. DeepEval and Giskard are additional application-evaluation candidates, with Giskard’s version distinction requiring particular care.
Use more than one layer when the system is important: quality and regression checks for release confidence, adversarial probes for known security classes, and human-led campaigns for context-dependent or high-impact risks. Treat every result as evidence to reproduce, triage, remediate, and retest—not as a universal safety certificate.
Frequently Asked Questions
Can I use these tools against a hosted model API?
Yes, where the project documents a compatible connector and you have the required credentials. Garak lists hosted routes including OpenAI API, AWS Bedrock, Replicate, LiteLLM, and REST, while Promptfoo supports connected model-comparison and application-evaluation workflows; verify current provider requirements before sending data.
Do open-source LLM red-team tools replace a security review?
No. Automated probes can expose useful leads, but they do not understand every business rule, authorization boundary, or harm scenario. Combine scans with threat modeling, code and configuration review, human analysis, and regression testing.
Should a clean Garak scan be reported as proof that an application is safe?
No. It means the selected probes and detectors did not produce a finding under the tested configuration. Coverage depends on adapters, probes, detector logic, application state, and the target’s changing behavior.
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Which version of Giskard should a new project use?
Check the current official project documentation and name the version explicitly in your plan. OWASP says Giskard v3 is a rewrite and v2 is no longer actively maintained, so an unqualified reference can lead to the wrong setup or support assumptions.
How do I turn a red-team finding into a lasting fix?
Preserve the exact input, conversation state, model, prompt, retrieval context, and tool actions. Reproduce the behavior, identify the control that failed, apply the mitigation, then add the case to a versioned regression suite so future releases are checked automatically.
