The Tool Desk
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Chainguard Harness is not a drop-in replacement for garak, PyRIT, or Promptfoo. It attacks a rebuilt, running application inside an isolated microVM, looks for exploitable runtime behavior, replays each finding for reproducibility, and supplies a code-specific remediation reviewed by a Chainguard engineer. The other tools operate at different layers: garak primarily probes a model or dialogue endpoint, PyRIT lets engineers compose programmable attack campaigns, and Promptfoo evaluates applications, RAG systems, and agents in repeatable test workflows.
The right choice depends on what you are testing: the model, an attack workflow, an application endpoint, or a production-like instance assembled from source. There is no authoritative cross-tool benchmark showing that one has the highest detection rate. Treat them as complementary security controls rather than four interchangeable scanners.
What Chainguard Harness actually tests
Chainguard describes Harness as a runtime vulnerability-hunting service. Instead of limiting a review to prompts or source inspection, it takes an application’s source, rebuilds the application in an isolated environment, starts the resulting instance, and allows a frontier model to probe the live system. The target is exploitable behavior in the application itself, including logic flaws, injection paths, and resource-exhaustion conditions.
The isolation boundary matters. The service is designed to attack an instance without placing the tester directly inside a customer’s ordinary development or production environment. When the model identifies a possible vulnerability, Harness replays the attack in fresh microVMs. A finding is therefore more than an anomalous response: it is expected to reproduce in a newly created environment before it is reported.
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Chainguard says the delivered report includes reproduction steps and a code-specific remediation. The proposed fix is reviewed by a Chainguard engineer, which distinguishes the service from a tool that stops at a score, alert, or generated attack transcript.
The Harness workflow
- Provide the application source. Harness uses the source as the input for its build rather than treating a model endpoint as the only target.
- Build in an isolated microVM. The service creates a contained environment intended to keep the assessment separate from the surrounding infrastructure.
- Boot a live instance. The assessment runs against the application while it is operating, so behavior that only appears at runtime can be exercised.
- Probe for exploitable behavior. A frontier model explores attack paths such as injection, flawed authorization or business logic, and resource exhaustion.
- Replay the candidate exploit. Harness repeats the attack in fresh microVMs to establish that the result is reproducible.
- Deliver remediation guidance. The report pairs reproduction instructions with a code-specific fix reviewed by a Chainguard engineer.
This workflow is aimed at a question that model scanners and prompt evaluators do not fully answer: “Can an attacker make this built application do something it should not do, and can the engineering team reproduce and repair it?”
How garak, PyRIT, and Promptfoo differ
garak: broad probes against a model or dialogue endpoint
garak is a scanner for weaknesses in language-model behavior. NVIDIA’s project description summarizes its purpose as checking whether an LLM can be made to fail in an unwanted way. Its probe and detector libraries cover categories such as hallucination, data leakage, prompt injection, misinformation, toxicity, and jailbreaks.
The practical unit under test is usually a model or dialogue endpoint. You select probes, send requests, and inspect detector results. That makes garak useful when the immediate concern is model behavior across a broad, known set of weakness classes. It is also a relatively low-configuration way to establish a baseline before investing in application-specific attack logic.
garak does not perform the Chainguard-style application rebuild, boot a customer application in a microVM, replay an exploit across fresh instances, or return a code-specific remediation reviewed by an engineer. A detector finding can show that a model produced an unsafe or undesirable response; it does not, by itself, prove that a particular deployed application is exploitable or identify the exact source change required.
PyRIT: programmable, multi-step red-team campaigns
PyRIT is a Microsoft-developed, MIT-licensed Python project listed by OWASP among open-source GenAI security testing tools. Its design centers on composing targets, converters, scorers, and orchestrated attacks. In other words, PyRIT provides building blocks for a security team that wants to define how an attack proceeds, how inputs are transformed, and how outcomes are scored.
This is the best fit of the four when the assessment requires bespoke control. A team can model a multi-turn conversation, chain several attack stages, adapt payloads between requests, or connect a custom scorer to an internal policy. The trade-off is engineering effort: PyRIT is a framework, not a one-command managed service that automatically rebuilds an application and writes a verified fix.
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Because the campaign is programmable, PyRIT can be aimed at a model, an application workflow, or another target that exposes a suitable interface. The resulting evidence depends on the campaign and scorers your team implements. That flexibility is valuable for unusual threats, but it also means teams must maintain their own orchestration, credentials, test data, and interpretation rules.
Promptfoo: configuration-driven application and regression testing
Promptfoo focuses on repeatable evaluations of applications, RAG systems, and agents. Its documented security use cases include dynamically generating application-specific attacks and integrating tests into CI/CD. A configuration-driven workflow lets a team run the same assertions and red-team checks whenever prompts, retrieval settings, tools, or application code change.
That application focus places Promptfoo between a pure model scanner and a managed runtime exploitation service. You can test the behavior of the system that wraps the model, including retrieval and agent workflows, rather than evaluating only the base model. CI/CD integration makes it suitable for regression gates: a build can fail when a known policy check, attack test, or expected behavior changes.
The comparison describing Promptfoo’s dynamic attacks and CI/CD, RAG, and agent capabilities is vendor-authored. Verify current enterprise features, limits, and commercial terms before selecting it for a procurement or compliance program. Promptfoo still differs from Harness in evidence and execution: its normal workflow runs configured evaluations against an endpoint, while Harness rebuilds source, attacks a live instance in an isolated microVM, replays exploits, and provides code-level remediation.
Side-by-side comparison
| Tool | Primary unit under test | Operating style | Strongest fit | Important limitation in this comparison |
|---|---|---|---|---|
| Chainguard Harness | Running application built from source | Managed runtime attacks in an isolated microVM, followed by exploit replay | Finding exploitable runtime behavior and receiving reproducible, code-specific remediation | Currently a beta Technology Preview with a waitlist; commercial terms and a public partner program are not stated |
| garak | Model or dialogue endpoint | Probe and detector libraries for known weakness classes | Broad, low-configuration model vulnerability scans | No application rebuild, Chainguard-style exploit replay, or code-specific fix report |
| PyRIT | Programmable target and attack workflow | Python components for targets, converters, scorers, and orchestrated campaigns | Custom and multi-step red-team campaigns | Requires engineering work and does not provide a managed runtime remediation service |
| Promptfoo | Application, RAG, or agent endpoint | Configuration-driven evaluations and red-team tests integrated with CI/CD | Application-specific regression gates and repeatable workflow testing | Confirm current enterprise terms and features; it does not provide Harness-style source rebuild and verified remediation |
Model testing versus application testing
The most important selection question is the boundary of the test. A model-oriented scan asks whether a model can be induced to produce a disallowed, misleading, or otherwise unsafe response. An application test asks whether the surrounding system mishandles input, retrieval results, permissions, tools, state, or business rules. A runtime assessment asks whether the built application can be exploited in operation.
- Choose garak for the model boundary. It is suited to broad probing of a model or dialogue endpoint across curated weakness categories.
- Choose PyRIT for the campaign boundary. It is suited to attacks whose sequence, transformations, scoring, or target adapters must be authored in Python.
- Choose Promptfoo for the application-workflow boundary. It is suited to repeatable checks around prompts, RAG retrieval, agent actions, and CI/CD changes.
- Choose Harness for the runtime boundary. It is suited to a source-built, live application where the desired evidence is a reproducible exploit and a code-specific repair.
These boundaries can overlap. For example, a RAG application may expose a model weakness detectable by garak, a retrieval-specific attack that belongs in Promptfoo, and an authorization flaw that only becomes obvious when a live instance is exercised. Calling all three “AI security tests” does not make their outputs equivalent.
Evidence quality: alerts, scores, and verified exploits
Each tool produces a different kind of evidence. garak commonly gives you probe outcomes and detector results. Those results are useful for identifying model behaviors that need mitigation, but they are not automatically application exploit proofs.
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PyRIT’s evidence is shaped by the campaign you build. A well-designed scorer and a multi-step transcript can provide rich context about attack progression, yet the quality and repeatability of the result depend on your implementation and target adapter.
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Promptfoo adds value by making application checks repeatable. A CI result can tell the team that a regression test now fails, that a generated attack crossed a policy boundary, or that an agent response changed after a code or prompt update. The result is valuable for release decisions, but it is not the same artifact as a replayed exploit against a freshly rebuilt instance.
Harness emphasizes a stronger claim about exploitability. Its process is designed to replay candidate findings in fresh microVMs and attach reproduction steps to the report. The remediation is intended to be specific to the affected code and reviewed by a Chainguard engineer. This does not make every other tool’s result unimportant; it means the tools answer different questions about confidence, reproducibility, and repair.
Can these tools run in CI?
garak in automated pipelines
garak’s probe-oriented design is compatible with repeatable automation. Teams can schedule scans against a model or dialogue endpoint and compare detector outcomes over time. Before making the scan a blocking gate, define which findings are release-critical and how to handle nondeterministic model responses. A broad probe library can generate useful discovery signals without being a stable pass/fail contract for every build.
PyRIT in automated pipelines
PyRIT can be incorporated into CI when the team owns the Python orchestration, target credentials, test fixtures, and scoring logic. This is powerful for custom attacks but creates maintenance work. Keep campaign inputs deterministic where possible, record the scorer version, and separate exploratory campaigns from gates that must produce a consistent result.
Promptfoo in CI/CD
CI/CD is a central Promptfoo use case. Configuration-driven tests can run when application code, prompts, retrieval configuration, or agent tools change. A practical rollout starts with non-blocking reports, then promotes a small set of high-confidence security assertions to required checks. Include RAG and agent scenarios when the application depends on them; model-only tests will not cover those integration paths.
Harness and pipeline placement
Harness is described as a managed service with a beta waitlist rather than as a local command-line scanner. Its natural placement is an assessment stage that receives a source revision, builds an isolated instance, and returns runtime findings and remediation guidance. The available material does not state a public CI integration contract, command syntax, or service-level terms, so confirm those details with Chainguard before designing a blocking pipeline gate.
Deployment and trust-boundary considerations
garak and PyRIT are open-source projects identified by OWASP, and both can be operated by a team that controls its own execution environment. That can simplify data-residency decisions, but the team remains responsible for model access, secrets, logging, dependency updates, and safe handling of generated attack content.
Promptfoo’s configuration-driven approach also supports team-controlled test execution, subject to the deployment model and plan selected. Review where prompts, retrieved documents, agent traces, and credentials are processed before sending sensitive data to any hosted component.
Harness uses a vendor-managed isolated environment for the build and runtime attack. Chainguard says the service is available through Anthropic’s Claude Marketplace, so customers may be able to use existing Anthropic commitments for procurement. Marketplace availability does not, by itself, establish data-retention, regional processing, support, or contractual terms; obtain those details before authorizing proprietary source or production-like data.
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Choose Chainguard Harness when runtime exploitability is the requirement
Select Harness when your central question is whether a real application build contains exploitable behavior that static review or endpoint probing could miss. It is especially relevant when the security team needs a reproducible attack path and a remediation tied to source code rather than a list of suspicious outputs. Account for the current beta Technology Preview status and waitlist when planning timelines.
Choose garak when you need breadth against model behavior
Use garak for an initial or recurring scan of a model or dialogue endpoint. Its curated probes cover many known weakness classes and require less bespoke engineering than a campaign framework. Treat the output as model-behavior evidence and follow important findings into application-level testing.
Choose PyRIT when the attack must be designed around your workflow
Use PyRIT when generic probes cannot express the threat. It is appropriate for multi-turn, adaptive, or organization-specific campaigns where you need to control conversions, scorers, target adapters, and orchestration in Python. Budget for implementation, test-data management, and ongoing campaign maintenance.
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Choose Promptfoo when regression control matters most
Use Promptfoo when application, RAG, or agent behavior must be checked repeatedly as part of delivery. Its configuration and CI/CD orientation make it a natural fit for release gates and regression suites. Validate current feature availability and enterprise terms for your required deployment.
When using more than one tool makes sense
A layered program can cover weaknesses that no single operating layer sees. One practical arrangement is:
- Model breadth with garak. Run broad probes to identify unsafe response classes and establish a baseline for the model or dialogue endpoint.
- Custom campaigns with PyRIT. Turn organization-specific threats into programmable, multi-step attacks with explicit scorers.
- Application regression with Promptfoo. Add tests for prompts, retrieval, tool use, and agent behavior to the CI/CD path so changes are evaluated continuously.
- Runtime exploitation with Harness. Periodically submit important application builds for isolated live-instance attacks, exploit replay, and code-specific remediation.
This sequence is an inference from the tools’ documented operating layers, not a published efficacy ranking. It also avoids forcing one tool to answer a question it was not designed to answer. A model scan can reveal a dangerous behavior; an application test can show whether controls contain it; a runtime assessment can establish whether the deployed build remains exploitable.
Limitations and questions to resolve before purchase
- Access: Harness is currently described as a beta Technology Preview with a waitlist. Confirm eligibility and expected onboarding time.
- Commercial scope: Public pricing, service limits, support commitments, and partner-program details are not stated in the available material.
- CI integration: Confirm whether Harness supplies an API, webhook, pipeline action, or other automation interface before treating it as a release gate.
- Data handling: Establish how source code, build artifacts, prompts, traces, and findings are retained and which regions process them.
- Result interpretation: Do not compare a garak detector score, a PyRIT scorer, a Promptfoo assertion, and a Harness exploit report as if they were the same metric.
- Benchmark claims: No authoritative cross-tool efficacy rate, false-positive rate, or adoption statistic is established here. Use capability and workflow fit instead of invented rankings.
Bottom line
Chainguard Harness is the specialist choice for runtime application security testing: it rebuilds source, attacks a live instance in an isolated microVM, replays findings, and reports a code-specific fix. garak is the broad model-probing option, PyRIT is the programmable campaign framework, and Promptfoo is the application and CI/CD regression layer. Teams with serious AI security requirements may reasonably use several of them, assigning each to the boundary it can test most directly.
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Can one of these tools replace all the others?
No. They operate at different layers: garak probes model behavior, PyRIT orchestrates custom campaigns, Promptfoo evaluates application workflows, and Chainguard Harness hunts exploitable behavior in a rebuilt running instance.
Is Chainguard Harness generally available?
Chainguard currently describes Harness as a beta Technology Preview with a waitlist. It also says the service is available through Anthropic’s Claude Marketplace.
Which tool is best for CI/CD regression checks?
Promptfoo is the clearest fit because its documented use case centers on configuration-driven application tests integrated with CI/CD. PyRIT and garak can be automated with engineering work, while Harness pipeline details should be confirmed directly.
Are there published benchmark scores proving one tool is better?
No authoritative cross-tool efficacy, false-positive, or adoption benchmark is established in the available material. Compare operating layer, automation model, evidence quality, and trust boundary instead.
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