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NVIDIA garak is the closest open-source, scanner-shaped alternative if you want to probe an LLM or an endpoint for prompt injection, jailbreaks, data leakage, and related failures. For repeatable checks in a development workflow, consider Promptfoo; for custom multi-turn or multimodal attack campaigns, Microsoft PyRIT is a better fit. Giskard and DeepTeam are useful options for Python-based tests involving agents and retrieval-augmented generation (RAG).
These tools do not reproduce Anthropic’s OSS Scanner. Anthropic’s opt-in service looks for vulnerabilities in eligible open-source projects, while most alternatives here test model behavior or AI application workflows. Pick by target layer, not by the word “scanner” in a tool’s description.
What Anthropic’s OSS Scanner does—and what “alternative” means
Anthropic announced its OSS Scanner on October 8, 2026, as a no-cost, opt-in service for open-source projects. Anthropic says the service periodically scans enrolled projects using its strongest models. Enrollment is by pull request, and eligibility is decided case by case using criteria similar to OSS-Fuzz. That means a maintainer cannot simply install a package and point it at any private repository; acceptance is part of the service.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsAnthropic says a report may include a reproducer, an explanation, a bisection to the commit that introduced the issue when possible, and a candidate patch. Those details matter because they connect a model-generated lead to a specific change and a possible remediation. The output is not a reviewed security finding: Anthropic states, “The outputs of this opt-in vulnerability scanner will be fully model-generated, without human review or triage.” Maintainers need to validate findings before acting on them.
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Anthropic reported more than 29,000 candidate vulnerabilities and approximately 6,000 manually reviewed. It also reported that 85 of 97 critical or high findings across 48 projects met the coordinated vulnerability disclosure (CVD) bar. Those figures are Anthropic’s reported results, not an independent benchmark of the scanner or of other tools. They should not be read as a detection-rate comparison.
That product combines a selective project-enrollment process with model-assisted discovery in open-source code. The alternatives below mostly serve a different purpose: they test a model or AI application for unsafe behavior. They can help a team assess prompt injection, jailbreaks, leakage, or agent scenarios, but they are not substitutes for reviewing source code, analyzing dependencies, designing authorization, or monitoring a deployed system.
Choose by the layer you need to test
Before selecting a tool, identify what is actually under test. “AI security testing” can mean scanning repository code, sending attacks to a model endpoint, testing a RAG pipeline, or exercising an agent through a sequence of actions. A tool that is useful at one layer may not cover another. Write the boundary down: the model, the API wrapper, retrieval and tool calls, the agent’s state, or the repository that implements them.
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| Need | Best fit among these options | What it is suited to |
|---|---|---|
| Quick baseline probes against a model or endpoint | NVIDIA garak | Command-line scanning with probe families for unwanted model behavior. |
| Repeatable checks alongside application and prompt changes | Promptfoo | Evaluation and red-team tests that can run in CI and provide pull-request feedback. |
| Custom, multi-step or multimodal attack campaigns | Microsoft PyRIT | Programmable attack orchestration, including custom flows, converters, and scorers. |
| Python evaluation suites for agents or RAG workflows | Giskard or DeepTeam | Scenario-based or category-aligned tests within an application-oriented evaluation workflow. |
| Managed workflows, runtime controls, or enterprise support | Commercial platforms such as HiddenLayer, Lakera, Mindgard, or Protect AI | Commercial security offerings that may combine scanning, red teaming, runtime controls, or governance capabilities. |
The table is a starting point, not a feature-equivalence chart. The available descriptions do not establish that any alternative uses Anthropic’s internal harness, model quality, eligibility rules, or free service. Open-source availability also does not tell you what infrastructure, model access, staffing, or review time a deployment will require.
NVIDIA garak: the closest open-source scanner for model behavior
Garak is an open-source LLM vulnerability scanner and the clearest first choice when the immediate task is to run a baseline against a model or an LLM-backed endpoint. Its documentation describes a mix of static, dynamic, and adaptive probes. Probe families cover hallucination, data leakage, prompt injection, misinformation, toxicity, and jailbreaks. That breadth lets a team begin with a broad behavioral check instead of assembling every test manually.
Garak runs from the command line and supports hosted APIs, REST endpoints, Hugging Face, Replicate, AWS Bedrock, LiteLLM, and local GGUF models. The same scanning approach can therefore be applied to a hosted service, a REST wrapper, or a local model, subject to the supported integration and the target’s configuration. Record which endpoint, model version, system prompt, and settings were used so a later run can be compared meaningfully.
When garak is a good fit
- You want an open-source LLM vulnerability scanner to probe a model or endpoint without building every test from scratch.
- You want to assess several behavior categories, such as injection, leakage, or jailbreak susceptibility.
- You need a command-line tool that can target a supported hosted or local model setup.
- You need an exploratory baseline before deciding which failures deserve a deeper, custom campaign.
Where garak stops
Garak is model- and endpoint-focused. It does not replace repository code review or authorization design. A model can pass a set of probes while the surrounding application still has an insecure permission check, an exposed secret, or a vulnerable dependency. Conversely, a probe that triggers an undesirable response is evidence worth investigating, not by itself a complete explanation of root cause or a validated patch.
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Do not treat a successful run as a certificate. Confirm whether the probe reached the production-like system boundary, whether defenses outside the model changed the response, and whether the behavior is reproducible. If the test sends sensitive prompts or retrieved documents to a hosted service, check the current data-handling defaults first.
Promptfoo: put red-team checks into CI
Promptfoo is a CLI and library for LLM evaluation and red teaming. OWASP lists it as MIT-licensed, with OWASP-mapped plugins and CI integration. Its strongest fit is a team that wants security checks to run beside prompt, model, and application regression tests as changes are made.
That workflow addresses a different problem from a periodic project scan. A pull-request test can help a team notice that a prompt or application change altered behavior. It can also make checks repeatable rather than relying on an occasional manual test. The practical value depends on selecting tests that match the application and reviewing what failures mean. Keep test inputs stable enough to compare runs, while still expanding them when a newly discovered attack needs coverage.
Choose Promptfoo when
- Security checks should run repeatedly during development rather than only during an exploratory assessment.
- You want evaluation and red-team checks close to prompt, model, or application regression tests.
- Pull-request feedback is more important than conducting a one-off scan.
- Your team can assign an owner to triage failed checks instead of allowing CI results to become ignored noise.
Promptfoo is not a source-code scanner or an authorization system. Passing its tests does not establish that every attack path has been covered. Treat CI results as a repeatable signal within a broader review, not as a security sign-off. A failing check should identify the exact test, target configuration, and expected-versus-observed behavior so developers can reproduce it.
Microsoft PyRIT: build programmable attack campaigns
Microsoft’s Python Risk Identification Tool, or PyRIT, is an open framework for orchestrating multi-turn and multimodal attacks against generative-AI systems. It is more useful to think of PyRIT as a programmable campaign framework than as a turnkey scanner with a fixed set of checks.
That distinction matters when a risk depends on a sequence. A single prompt may not expose a weakness that appears only after an agent has received context, taken an action, and been prompted again. PyRIT is a fit when a security team wants to shape attack flows and use custom converters or scorers rather than rely only on a ready-made probe catalog. The campaign should define its starting state, allowed actions, stopping conditions, and what constitutes a finding.
Choose PyRIT when
- Your assessment needs multi-turn or multimodal attack orchestration.
- You need to customize the flow, how inputs are transformed, or how outcomes are scored.
- You have the capacity to build and maintain a tailored campaign rather than expecting a simple scan-and-report experience.
- You need to model escalation across turns, tools, or media rather than test isolated text responses.
That flexibility also means PyRIT is not interchangeable with a quick endpoint scanner. A programmable framework can support a more tailored assessment, but the team must decide what to test and how to interpret the result. PyRIT does not replace source-code scanning or application authorization controls. Document campaign assumptions so another reviewer can tell whether a result reflects a real application path or only a laboratory setup.
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Giskard and DeepTeam: test Python applications, agents, and RAG
Giskard and DeepTeam are relevant when the target is more than a bare model endpoint and tests need to fit into a Python-oriented evaluation workflow. OWASP lists Giskard as an open-source testing library for LLM agents, with scenario-based checks and vulnerability scanning. It lists DeepTeam as an open-source red-teaming framework aligned with OWASP LLM vulnerability categories.
Giskard for scenario-based agent checks
Giskard is worth considering when an agent’s behavior across a scenario is the concern. Testing an agent means evaluating how the application uses the model, not only whether the underlying model answers a particular prompt safely. Scenario-based checks can be a better conceptual fit for those workflows than treating the agent as a single endpoint. Include the retrieval context and tool interactions that are part of the scenario; otherwise a passing test may describe only an incomplete path.
DeepTeam for category-aligned red teaming
DeepTeam is an option when a team wants an open-source red-teaming framework organized around OWASP LLM vulnerability categories. That makes it suitable for building tests around an application’s risk areas rather than relying on a single model-behavior scan. Map each category to the actual controls and data flows in the application so the test has an actionable owner.
Both options fit Python, RAG, and agent contexts better than a simple endpoint scan when the application workflow itself is central. The descriptions do not establish that either tool provides the same repository-level analysis, enrollment, or reporting process as Anthropic’s service. They should be treated as components of an evaluation suite, not as proof that the underlying code and dependencies are secure.
Commercial enterprise platforms: a different kind of alternative
OWASP’s commercial category includes HiddenLayer for model scanning and runtime detection, Lakera for runtime guardrails and red teaming, Mindgard for automated AI red teaming and continuous security testing, and Protect AI for AI/ML security that includes model scanning and red teaming.
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How to compare tools without confusing coverage
A useful comparison is not simply “which scanner finds the most issues?” The tools described here target different layers and testing styles. Compare them across five dimensions before putting one into a security process.
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- Target layer: Is the tool assessing repository code, a model endpoint, a RAG pipeline, or an agent’s trajectory? Do not infer code scanning from model probing. Record the exact boundary and integrations required.
- Attack depth: Does it run individual probes, or can it orchestrate multi-turn and multimodal campaigns? A deeper campaign may reveal sequence-dependent behavior but generally requires a clearer test design and more deliberate interpretation.
- Automation: Is the tool primarily a local CLI, a Python library, or a CI-integrated workflow? Choose based on where the team can consistently run and review tests. An unused scheduled scan provides less value than a narrower check that has an owner.
- Evidence quality: What does a finding contain—reproduction steps, scoring, deduplication, or patch guidance? Anthropic says its reports can include a reproducer, explanation, a commit bisection where possible, and a candidate patch; do not assume another tool provides the same evidence.
- Operational controls: Does the approach address runtime detection, access controls, telemetry, or human review? A red-team test harness alone does not provide all of those controls.
Also compare the practical operating burden: model or API access, test duration, log storage, review capacity, and handling of prompts or retrieved data. No common price or detection benchmark is established for the named projects. Anthropic’s service is described as no-cost only for eligible enrolled projects; commercial offerings require direct pricing and terms checks, while open-source tools still require a team to run and interpret them.
DOE guidance warns that Promptfoo, PyRIT, and garak are not source-code scanners or authorization systems and do not guarantee complete attack coverage. That is a useful boundary for interpreting test results: these tools can expose specific behaviors, but they cannot certify the security of the whole system.
A practical way to choose and use an alternative
Start with the question you need answered, then select the narrowest tool that can test it. Avoid treating a collection of successful probe results as proof that risks outside the tested layer have been addressed.
- Decide whether your target is code or behavior. If you want to find vulnerabilities in an open-source repository, the alternatives described here do not establish the same repository-scanning service Anthropic offers. If you want to test how an LLM application responds to attacks, continue with a behavior-testing tool.
- Write down the system boundary. Identify whether the test target is a model, REST endpoint, RAG application, or agent workflow. This determines whether a baseline scanner, CI suite, or programmable campaign is the more natural fit.
- Choose a controlled test environment. Use a target and credentials that can safely receive adversarial prompts. Note the model, endpoint, prompts, retrieval corpus, tools, and configuration so a result can be repeated.
- Match the tool to the operating model. Use garak for a fast baseline against a supported model or endpoint; Promptfoo when checks should accompany changes in CI; PyRIT when the campaign needs custom, multi-step or multimodal orchestration; and Giskard or DeepTeam when Python application scenarios, agents, or RAG are central.
- Run tests against an appropriate target and review the evidence. A triggered probe or scenario is a lead to investigate. Validate whether it reflects a real application risk, what conditions are required, and whether the issue lies in the model behavior or surrounding application. Capture reproductions and expected-versus-observed results where the tool supports them.
- Address risks outside the harness. Review source code and dependencies, design authorization controls, and consider runtime monitoring. A red-team harness is one part of that process, not a replacement for it.
- Check current project details before adopting. Tool capabilities and maintenance status can change quickly. Verify current releases and data-handling defaults before sending prompts, application context, or other potentially sensitive material to a tool or service.
What no alternative here promises
Anthropic’s scanner is selective and opt-in; its no-cost service is not automatically available to every maintainer. A self-serve model-testing tool can help teams that need to probe an endpoint, but it does not thereby become an equivalent repository scanner.
Likewise, a tool’s open-source status or category coverage does not establish complete attack coverage, human triage, a validated remediation, or runtime protection. The available descriptions establish the broad roles of the tools, but not a common benchmark, comparative detection rate, pricing comparison, or equivalence to Anthropic’s internal service. Make the choice based on your target and workflow, and treat findings as evidence to validate.
Frequently Asked Questions
Is Anthropic OSS Scanner available to every open-source project?
No. It is an opt-in service, enrollment is by pull request, and Anthropic decides eligibility case by case using criteria similar to OSS-Fuzz.
Which alternative is closest to a self-serve scanner for an LLM endpoint?
NVIDIA garak. It is an open-source, command-line LLM vulnerability scanner with probes for hallucination, data leakage, prompt injection, misinformation, toxicity, and jailbreaks, and it supports hosted and local model integrations.
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Which tool should I use for CI and pull-request checks?
Promptfoo is the practical fit when evaluation and red-team checks should run beside prompt, model, and application regression tests in CI.
What should I use for multi-turn or multimodal attacks?
Microsoft PyRIT. It is a programmable framework for orchestrating multi-turn and multimodal campaigns, with customizable flows, converters, and scorers.
Are Giskard and DeepTeam endpoint scanners?
They are better suited to Python evaluation workflows involving agents and RAG. Giskard emphasizes scenario-based agent checks and vulnerability scanning; DeepTeam is aligned with OWASP LLM vulnerability categories.
Do these tools replace source-code review or authorization controls?
No. DOE guidance specifically warns that garak, Promptfoo, and PyRIT are not source-code scanners or authorization systems and do not guarantee complete attack coverage.
Are Anthropic’s scanner findings reviewed by people?
Anthropic says the scanner’s outputs are fully model-generated without human review or triage. Maintainers must validate findings before acting on them.
Can I compare the tools by price or detection rate?
No common pricing table or independent detection benchmark is established here. Anthropic describes its service as no-cost for eligible projects; commercial platforms require direct pricing checks, and open-source tools still require operating and review resources.
What should I verify before connecting a tool to sensitive data?
Check the current release, maintenance status, supported integrations, and data-handling defaults. Confirm what prompts, retrieved documents, logs, or application context will leave your environment before running tests.
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Use garak for a fast open-source baseline against a model or endpoint, Promptfoo for repeatable CI checks, PyRIT for programmable multi-turn or multimodal campaigns, and Giskard or DeepTeam for Python agent and RAG scenarios. None is a drop-in replacement for Anthropic’s selective repository-scanning service, and none replaces code review, dependency analysis, authorization, or runtime monitoring.
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