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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Chainguard Harness is the outlier in this comparison. It attacks a running application rebuilt from source inside an isolated microVM, uses a frontier model to search for exploitable behavior, replays any finding until it is reproducible, and supplies code-specific remediation reviewed by a Chainguard engineer. garak, PyRIT, and Promptfoo generally operate one layer earlier: they test a model endpoint, orchestrate attack campaigns, or evaluate an application endpoint through repeatable configurations.
That does not make Harness universally “better.” Choose it when you need evidence that a vulnerability works in a real application build and a fix has been validated. Choose garak for broad, low-configuration model probing; PyRIT for programmable, multi-step campaigns; and Promptfoo for application, RAG, or agent tests that run as recurring CI/CD gates. A mature security program can use several because they answer different questions.
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What Chainguard Harness actually tests
Chainguard describes Harness as a runtime vulnerability-hunting service rather than a conventional model scanner. You provide application source, and the service rebuilds it in an isolated microVM. It then boots that build as a live instance and gives a frontier model an opportunity to probe the running system.
The target is exploitable application behavior: logic flaws, injection paths, resource-exhaustion conditions, and other weaknesses that may not appear in a static review or a test aimed only at the language model. The distinction matters for AI applications because a secure base model can still be exposed through unsafe tool permissions, weak authorization, untrusted data flows, or business logic in the surrounding code.
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When the model finds a possible issue, Harness attempts the attack again in fresh microVMs. A finding is not treated as useful merely because one unusual prompt produced an alarming response; it is expected to land reproducibly. The delivered report includes reproduction steps and a code-specific remediation reviewed by a Chainguard engineer. Chainguard’s own description summarizes the workflow: “Chainguard Harness builds your application from source in an isolated environment and lets a frontier model hunt through a live instance for exploitable vulnerabilities. Each finding is reproducible and delivered with a fix.”
Harness is currently a beta Technology Preview with a waitlist. Chainguard also says it is available through Anthropic’s Claude Marketplace, so eligible customers may use existing Anthropic commitments for the service. Public commercial terms and details of a broader partner program are not stated, so procurement, data handling, supported build systems, and service limits should be confirmed before adoption.
How the four tools differ at a glance
| Tool | Primary unit under test | Operating style | Best fit | Important limitation |
|---|---|---|---|---|
| Chainguard Harness | A running application built from source | Builds and boots the app in an isolated microVM, launches model-driven attacks, replays exploits, and validates remediation | Runtime bugs and exploitable behavior that model-only or static checks can miss | Beta access; public pricing, service limits, and partner terms are not stated |
| garak | A model or dialogue endpoint | Probe and detector library covering known weakness classes | Broad, repeatable scans with comparatively little custom engineering | Does not rebuild your application, replay an exploit in isolated instances, or provide a Chainguard-style code fix |
| PyRIT | An attack campaign and its target workflow | Python framework for composing targets, converters, scorers, and orchestrated attacks | Custom and multi-step red-team campaigns requiring programmable control | Teams must design, maintain, and interpret the campaign; it is not a one-command remediation service |
| Promptfoo | An application, RAG system, or agent endpoint | Configuration-driven evaluations and red-team tests that can be integrated with CI/CD | Application-specific regression gates and recurring tests for RAG and agent behavior | Feature and enterprise details change; verify current terms and capabilities directly |
The table describes different operating layers, not a universal ranking. An alert from garak, a score from Promptfoo, a custom PyRIT campaign result, and a Harness exploit report are different kinds of evidence and should not be compared as if they were the same metric.
Chainguard Harness workflow, step by step
1. Supply a buildable application
Harness starts with source rather than a detached prompt endpoint. The practical prerequisite is a reproducible build: dependency definitions, configuration needed to start the service, and a clear entry point. Treat secrets and production data as out of scope unless Chainguard’s current service documentation explicitly supports them; the stated model is an isolated test environment, not a license to expose live credentials.
2. Rebuild inside an isolated microVM
Chainguard says the application is rebuilt in an isolated microVM. Isolation gives the attack process a boundary and makes repeated runs more comparable. It also means the behavior you observe is tied to the supplied source and build configuration. If your local deployment depends on an external queue, database, identity provider, or proprietary model gateway, document those dependencies and determine how the preview handles them before treating a clean run as proof of production safety.
3. Boot the live instance
The service launches the rebuilt application as a running instance. This is the point at which Harness differs most sharply from a model-only scanner: routes, authorization checks, tool calls, parsers, and resource controls can interact. A vulnerability can require a sequence of requests or a particular state transition rather than a single malformed prompt.
Rank #2
4. Let a frontier model search for exploitable behavior
A frontier model probes the live target for weaknesses such as injection, logic errors, and resource exhaustion. The model is acting as an attacker against your application, not as the application being graded for general helpfulness. That perspective is useful for agent systems in which the dangerous outcome comes from the orchestration code, a tool, or a data connector.
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5. Replay candidate findings
Harness replays findings in fresh microVMs. Repetition addresses a common problem in AI security testing: a single stochastic response may be interesting but not actionable. Reproduction provides a stronger basis for triage, assignment, and a regression test. It does not guarantee that every undiscovered issue is absent; it establishes that the reported behavior can be triggered again under the tested build and environment.
6. Receive remediation tied to the code
The report includes reproduction steps and a code-specific fix reviewed by a Chainguard engineer. That closes more of the loop than a generic “prompt injection detected” label. Your team still needs to review the proposed change, run its own tests, and verify that the fix does not break an intended workflow or merely block the exact replay string.
What garak is good at
garak is a free scanner focused on whether a large language model can be made to fail in an unwanted way. Its probe and detector approach covers a broad set of weakness classes, including hallucination, data leakage, prompt injection, misinformation, toxicity, jailbreaks, and related failures. Because it targets a model or dialogue endpoint, it is useful early in a project and as a recurring breadth check when you have many models or providers to assess.
Strengths
- Broad, curated coverage of known model failure patterns.
- A repeatable scan model that is easier to schedule than a bespoke red-team exercise.
- Low configuration compared with writing a full campaign framework.
- Free availability, making it practical for exploratory testing and developer environments.
Boundaries
- A model-level result does not prove that a particular application route is exploitable.
- It does not rebuild your source, exercise your complete business workflow, or replay an exploit across isolated application instances.
- Its findings do not include a code-specific remediation reviewed by an engineer.
Use garak to ask, “How does this model respond to a known class of adversarial input?” Do not use a clean garak scan as evidence that your agent’s authorization, retrieval, tool-use, or rate-limiting logic is secure.
What PyRIT is good at
PyRIT is a Microsoft-developed, MIT-licensed Python project for constructing red-team campaigns. OWASP’s testing guidance lists it among open-source generative-AI security tools. Instead of prescribing one fixed scan, PyRIT lets a security engineer compose targets, converters, scorers, and orchestrated attacks.
Rank #3
When its programmability matters
Suppose an attack must begin with a benign conversation, transform payloads into several formats, call a retrieval tool, score the resulting output, and branch when a condition is met. That is campaign logic, not a single probe. PyRIT gives a team a place to encode that logic and preserve it as a repeatable test suite.
Costs of flexibility
PyRIT requires engineering work. You must decide what to target, how to convert inputs, which outcomes count as failure, how to handle credentials and rate limits, and how to store evidence. A sophisticated campaign can produce deeper insight than a canned scan, but maintenance becomes part of the security program. PyRIT also does not automatically rebuild an application in a microVM, replay an exploit as a service, or return a reviewed patch.
What Promptfoo is good at
Promptfoo uses configuration-driven evaluations and red-team tests for applications, retrieval-augmented generation (RAG) systems, and agents. Its documented use cases emphasize running application-specific checks repeatedly in CI/CD, where a change can be blocked when a regression crosses a defined threshold or violates a policy.
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Promptfoo is a natural fit when the team already has an endpoint and wants to test prompts, providers, retrieval settings, tools, or agent behavior after every change. Tests can be kept with the application configuration and reviewed alongside code. This makes failures visible to developers before release rather than only during an occasional security exercise.
What it does not establish by itself
A configuration-driven test tells you how the application behaved for the cases you defined and the generated attacks it produced. It is not the same evidence as Harness reproducing an exploit in fresh isolated microVMs and attaching a code-specific fix. Promptfoo’s published comparison with garak is vendor-authored, so claims about relative coverage, generated attacks, or enterprise features should be checked against the current product documentation before procurement.
Compare the tools on four decision axes
1. Unit under test
- Model endpoint: garak is the clearest fit when the question concerns a model’s generic failure modes.
- Programmable campaign: PyRIT is appropriate when the target and attack sequence need custom Python logic.
- Application, RAG, or agent endpoint: Promptfoo focuses on behavior your product exposes and can be evaluated repeatedly.
- Running application instance: Harness tests the assembled system built from source, including runtime interactions and business logic.
2. Automation shape
garak provides probes and detectors; PyRIT provides orchestration primitives; Promptfoo provides configuration-driven evaluations and CI/CD integration; Harness provides a managed runtime attack workflow. These shapes affect who owns test design. A platform team may standardize Promptfoo configuration, a red team may author PyRIT campaigns, and a product security service may consume Harness reports without maintaining every exploit strategy internally.
3. Evidence quality
Model scans commonly produce alerts, detector results, or scores. Those are valuable for breadth and trend tracking but can require application-specific triage. Harness aims for a stronger exploit narrative: a running target, replayable behavior, reproduction steps, and remediation tied to source code. “Stronger” does not mean complete; it means the evidence is closer to a demonstrated runtime vulnerability.
4. Deployment and trust boundary
garak and PyRIT are open-source-oriented tools that can be executed under your own controls. Promptfoo can be integrated into your delivery pipeline according to its current deployment options. Harness uses a vendor-managed isolated environment and is currently a beta service. Before sending source or configuration, establish what data leaves your boundary, how long artifacts are retained, which regions are supported, and how access is controlled. The available public description does not state those commercial or operational details.
Can these tools run in CI?
Yes, but “run in CI” means something different for each tool.
- garak: schedule repeatable probe runs against a test model endpoint and fail or warn on selected detector results.
- PyRIT: invoke your Python campaign from the pipeline, collect its scorers and evidence, and define the pass/fail policy yourself.
- Promptfoo: keep evaluation configuration with the application and use its documented CI/CD integration for regression gates.
- Harness: treat the service as a build-and-attack job. Supply a testable source revision, wait for the isolated runtime assessment, and route reproducible findings into the normal remediation workflow. Because it is a beta Technology Preview, confirm current automation interfaces and waitlist access before designing a hard release dependency.
A sensible pipeline separates fast checks from expensive runtime exercises. Run model probes and configuration tests on pull requests or nightly schedules; run full isolated runtime attacks on release candidates or a recurring security cadence. Preserve the tested commit, configuration, model version, and environment with each result so a later comparison is meaningful.
Which tool should you choose?
Choose Chainguard Harness when
- The requirement is to find exploitable behavior in the assembled application, not merely undesirable model output.
- You need a reproducible attack narrative that developers can replay.
- A code-specific remediation reviewed by an engineer is more valuable than a generic alert.
- You can provide source and accept a vendor-managed isolated test environment.
- Beta access and Marketplace procurement fit your schedule and contractual requirements.
Choose garak when
- You need broad coverage of known model weakness classes quickly.
- The immediate target is a model or dialogue endpoint.
- You want a free scanner for exploratory work, model selection, or recurring baseline checks.
- Your team can triage model-level alerts separately from application vulnerabilities.
Choose PyRIT when
- The attack requires multiple turns, transformations, branching, or custom scoring.
- You have Python expertise to maintain a campaign as application behavior changes.
- You want to encode a research or red-team methodology rather than adopt a fixed probe set.
- You need control over targets, converters, scorers, and orchestration.
Choose Promptfoo when
- Application-specific tests must run repeatedly in CI/CD.
- Your product includes RAG or agent workflows whose behavior must be regression-tested.
- Developers need configuration that can be reviewed and updated with the application.
- You want a practical gate between a code change and deployment, while accepting that test results still require security interpretation.
When using more than one tool makes sense
These tools are complementary because they observe different layers. A layered program can use garak for model-oriented breadth, PyRIT for bespoke campaigns, Promptfoo for application regression gates, and Harness for isolated runtime exploitation and fix validation.
- Establish a model baseline with garak. Record weakness classes that matter to the selected model and provider.
- Encode high-risk workflows in PyRIT. Add multi-step attacks involving conversation state, conversions, tools, or custom scoring.
- Put product-specific regressions in Promptfoo. Run them when prompts, retrieval indexes, agent tools, or orchestration code changes.
- Use Harness on release candidates or high-value services. Let it attack the built application and require reproducible findings to enter remediation.
- Feed fixes back into the cheaper layers. Turn a confirmed runtime issue into a regression case so future builds fail earlier.
The combination is an inference from the tools’ documented operating layers, not a claim that every organization needs all four. Small teams may begin with one layer and add another when a specific blind spot appears.
Common mistakes and how to avoid them
Calling a model scan an application security assessment
A jailbreak or prompt-injection result from garak can be important, but it does not demonstrate that your application’s authorization boundary can be crossed. Pair model findings with endpoint and workflow tests.
Assuming generated attacks are automatically complete
Promptfoo and model-driven services can generate useful cases, while PyRIT can orchestrate sophisticated campaigns. None proves that every attacker path was explored. Define assets, trust boundaries, and high-impact actions explicitly, then review coverage.
Ignoring reproducibility
Record the exact build, model, configuration, prompts, tools, and external dependencies. For Harness findings, use the supplied reproduction steps and verify the behavior against the same revision before changing code.
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Sending production secrets into a test service
Use synthetic credentials and controlled data unless the service’s current terms explicitly authorize another arrangement. A security test should not create a second incident through unnecessary data exposure.
Blocking releases on noisy scores
Set CI policies around impact and reproducibility, not a single unexplained number. A warning may be appropriate for a low-confidence detector result; a confirmed exploit affecting a privileged tool should receive a release-blocking response.
Treating a proposed fix as automatically correct
Even a code-specific remediation reviewed by an engineer needs application-owner review, unit and integration tests, and a rerun of the attack. Confirm that the patch removes the vulnerable path rather than only changing one prompt or payload.
What is not established in a cross-tool ranking
No authoritative source in this comparison provides a common benchmark for efficacy, false-positive rate, coverage percentage, adoption, or time to remediation across Harness, garak, PyRIT, and Promptfoo. A numerical leaderboard would therefore be misleading. Evaluate the tools using your own representative applications, attack goals, evidence requirements, and operating constraints.
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Bottom line
Chainguard Harness is the best fit when you want a vendor-assisted runtime attack against a real application build, reproducible exploit steps, and a remediation tied to source code. garak is the efficient breadth scanner for model behavior. PyRIT is the programmable framework for custom attack campaigns. Promptfoo is the practical choice for application, RAG, and agent evaluations that must recur in CI/CD. They are different instruments: select the one that matches the layer you need to test, and combine layers when a single test cannot establish the evidence your risk decision requires.
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