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NVIDIA garak is the closest self-serve replacement for Anthropic’s OSS Scanner when you need to probe an LLM or API quickly. Choose Promptfoo when security tests must run in CI, Microsoft PyRIT for programmable multi-turn or multimodal attacks, and Giskard or DeepTeam for Python-based RAG and agent testing. Enterprise teams that need managed workflows, runtime controls or governance can evaluate HiddenLayer, Lakera, Mindgard or Protect AI.
Those tools do not recreate Anthropic’s eligibility process, internal models or free service. They address different layers of an AI system, so the right choice depends on whether you are testing an endpoint, a repository, a retrieval pipeline, an agent trajectory or production runtime behavior.
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What Anthropic’s OSS Scanner actually provides
Anthropic announced OSS Scanner on October 8, 2026 as an opt-in service for qualifying open-source projects. Anthropic decides eligibility case by case using criteria similar to OSS-Fuzz, and enrollment happens through a pull request. Selected projects receive periodic scans by Anthropic’s strongest models at no cost.
A report can include a reproducer, an explanation of the issue, a bisection to the introducing commit where possible, and a candidate patch. That evidence can shorten the path from discovery to a fix, but maintainers remain responsible for validating every finding. Anthropic states in its Frontier Red Team launch post that “The outputs of this opt-in vulnerability scanner will be fully model-generated, without human review or triage.”
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Anthropic’s launch figures are useful context rather than an industry benchmark: more than 29,000 candidate vulnerabilities were reported, approximately 6,000 were manually reviewed, and 85 of 97 critical or high findings across 48 projects met the coordinated vulnerability disclosure (CVD) bar. Those numbers describe Anthropic’s reported program results, not the total number of vulnerabilities in open-source software or the comparative accuracy of other scanners.
The service is therefore valuable when your project is accepted and you want periodic, model-assisted repository analysis. It is not a universal endpoint tester, an always-on production monitor, an authorization system or a guarantee that every attack path will be found. Selectivity and the absence of human triage in the generated output are the two reasons many teams need another tool.
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How to compare an alternative
Before choosing a product, identify the layer and the evidence you need. A model probe can reveal that an endpoint follows a jailbreak, while a source scanner can reveal an unsafe deserialization path; neither result proves that the other layer is secure.
| Comparison axis | Questions to ask | Why it changes the choice |
|---|---|---|
| Target layer | Does it inspect a source repository, model endpoint, RAG pipeline or agent trajectory? | Tools built for endpoints generally do not analyze repository permissions or application authorization. |
| Attack depth | Are tests single probes, adaptive sequences, multi-turn conversations or multimodal campaigns? | Simple probes establish a baseline; chained attacks expose escalation and state-handling failures. |
| Automation | Can it run as a local CLI, a Python library, or a pull-request and CI job? | Repeatable automation catches regressions after prompts, models or application code change. |
| Evidence quality | Does it provide reproduction steps, scoring, deduplication, a severity rationale or patch guidance? | Security teams need actionable evidence, not a list of blocked or unblocked prompts. |
| Operational controls | Does it provide runtime detection, access controls, telemetry, workflow management or human review? | Testing before release does not replace controls that limit damage in production. |
NVIDIA garak: the closest scanner-shaped alternative
Garak is an open-source LLM vulnerability scanner and the most direct starting point for a self-serve baseline. Its documentation describes a combination of static, dynamic and adaptive probes that test whether an LLM-backed application can be made to fail in unwanted ways.
What garak tests
Probe families cover hallucination, data leakage, prompt injection, misinformation, toxicity and jailbreaks. The breadth makes garak useful for a first pass against a new model or endpoint, especially when you need a repeatable set of known attack ideas rather than a bespoke campaign.
Where it can connect
Garak runs from the command line and supports hosted APIs, REST endpoints, Hugging Face, Replicate, AWS Bedrock, LiteLLM and local GGUF models. That range lets a team test a cloud model, an internal gateway and a local model with the same scanner-oriented workflow.
When garak is the right fit
- You need an open-source tool that can start probing an endpoint without building a campaign framework.
- You want coverage across common LLM failure classes, including injection and jailbreak behavior.
- You need to compare model or endpoint behavior during a security baseline or before a release.
What garak does not replace
Garak is model- and endpoint-focused. It does not replace repository code review, dependency analysis, authorization design, secret management or production telemetry. A clean probe run means only that the tested probes did not produce the recorded failures under those conditions. It does not establish that an application is safe against unseen prompts, business-logic abuse or a vulnerable dependency.
Promptfoo: the practical choice for CI regression testing
OWASP lists Promptfoo as a MIT-licensed command-line tool and library for LLM evaluation and red teaming. Its OWASP-mapped plugins and CI integration make it a strong fit when security checks belong beside ordinary prompt, model and application regression tests.
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Why teams put it in the pull-request path
A CI-oriented test suite can run whenever a prompt template, model, retrieval configuration or application change is proposed. The important benefit is repeatability: the same assertions and attack cases can be evaluated on every change, producing feedback while the change is still under review.
Use Promptfoo when
- The primary requirement is a repeatable pass/fail or score in CI rather than an exploratory one-time scan.
- Developers and security engineers need to share test cases in the same repository.
- You want security checks mapped to recognized LLM risk categories and visible in normal build results.
Designing useful CI tests
Keep a small, stable gate for high-impact behaviors such as prompt injection, data leakage and unsafe tool use, then run a broader suite on a schedule. Record the model version, system prompt revision, retrieval corpus version and application commit with each result. Otherwise, a changed dependency or model can look like a security regression when the test environment simply changed.
Promptfoo is a testing and red-teaming layer. It is not a source-code scanner or an authorization system, and a passing pipeline cannot guarantee complete attack coverage.
Microsoft PyRIT: programmable multi-turn and multimodal campaigns
OWASP describes PyRIT, Microsoft’s Python Risk Identification Tool, as an open framework for orchestrating multi-turn and multimodal attacks against generative-AI systems. It is better understood as a programmable campaign framework than as a turnkey scanner.
What makes a campaign different from a probe
A single probe asks whether one input produces an unwanted response. A campaign can maintain conversation state, transform an attack between turns, score intermediate outputs and escalate after a partial success. That is important for systems whose weakness appears only after an attacker earns trust, changes the topic or combines text with another modality.
Choose PyRIT when
- Security engineers need custom attack flows instead of a fixed probe catalog.
- You need converters, scorers or orchestrated steps tailored to your application.
- The target exposes stateful conversations, multimodal input or tool calls that require several stages to reach a meaningful outcome.
Operational considerations
Because PyRIT is programmable, the quality of the campaign depends on the prompts, converters, scoring logic and stop conditions your team writes. Define scope and rate limits before running adaptive attacks, isolate test credentials, and preserve the full conversation and tool-call transcript as evidence. PyRIT can exercise a system deeply, but it does not automatically review your source code or enforce production authorization.
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Giskard and DeepTeam for Python, RAG and agent scenarios
These two projects are particularly relevant when the thing under test is more than a bare model endpoint.
Giskard
OWASP lists Giskard as an open-source testing library for LLM agents with scenario-based checks and vulnerability scanning. Scenario tests can represent the way an agent retrieves documents, reasons over them and uses tools, rather than treating every response as an isolated completion.
Giskard is a natural fit for a Python evaluation suite that already has fixtures, datasets and assertions. Use it when you want vulnerability checks to live alongside functional tests for a RAG or agent workflow. Define scenarios that exercise trusted and untrusted documents, conflicting instructions and tool-call boundaries, then inspect whether the resulting behavior violates the application’s intended policy.
DeepTeam
OWASP lists DeepTeam as an open-source red-teaming framework aligned with OWASP LLM vulnerability categories. It suits teams that want a structured red-team layer in Python and need to organize attacks around recognized vulnerability classes.
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Choosing between them
- Choose Giskard when scenario-based agent checks and vulnerability scanning need to sit inside an existing Python evaluation workflow.
- Choose DeepTeam when you want an OWASP-aligned red-teaming structure for broader attack planning.
- Use either with endpoint probes and source review when the application combines an agent with ordinary software components.
Commercial platforms for managed security operations
Open-source frameworks provide flexibility, but some organizations need managed workflows, runtime controls, support or governance features. OWASP’s commercial category includes several different emphases:
| Platform | Primary emphasis described by OWASP | Where it may fit |
|---|---|---|
| HiddenLayer | Model scanning and runtime detection | Teams that need visibility before deployment and detection while models operate. |
| Lakera | Runtime guardrails and red teaming | Applications that need controls at the point where prompts and responses pass through production systems. |
| Mindgard | Automated AI red teaming and continuous security testing | Organizations seeking recurring, managed testing rather than a project-specific scan. |
| Protect AI | AI/ML security, including model scanning and red teaming | Programs covering broader machine-learning assets as well as generative-AI testing. |
These products are commercial complements or alternatives, not interchangeable open-source repositories. Compare data handling, deployment model, supported integrations, evidence retention, human review, runtime enforcement and the scope of support before sending sensitive prompts, source or model artifacts to any hosted service.
Which alternative fits your situation?
| Your immediate need | Best starting point | Reason |
|---|---|---|
| Fast baseline against a model or API | NVIDIA garak | Scanner-oriented probes cover common LLM failure classes and multiple hosted or local targets. |
| Security checks on every pull request | Promptfoo | CLI and library integration supports repeatable evaluation and CI feedback. |
| Stateful, adaptive or multimodal attack research | Microsoft PyRIT | Programmable orchestration supports custom converters, scorers and multi-step escalation. |
| Python tests for an agent or RAG workflow | Giskard | Scenario-based checks and vulnerability scanning are designed for agent behavior. |
| OWASP-aligned Python red teaming | DeepTeam | Organizes attacks around LLM vulnerability categories for structured campaigns. |
| Managed testing, runtime controls or governance | Evaluate commercial platforms | Managed workflows and operational features address needs beyond a local test harness. |
If you are unsure, start with garak for an endpoint baseline and add Promptfoo when the cases that matter have stabilized. Move to PyRIT when a weakness requires several turns or modalities. Add Giskard or DeepTeam when retrieval and agent orchestration become the system under test, not just the model.
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1. Establish an endpoint baseline
Inventory every model entry point, including public APIs, internal gateways and local deployments. Run a scanner such as garak against each approved target with non-production credentials and a defined request budget. Save the model identifier, endpoint configuration, probe set, timestamp and raw outputs so a later run can be compared fairly.
2. Turn important failures into regression tests
For each confirmed issue, create a minimal reproducer and an expected-safe assertion. Put those cases in Promptfoo or the Python test framework already used by the service. Run the small high-value set on every change and a broader set on a schedule; this avoids making every pull request depend on a slow exploratory campaign.
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3. Exercise escalation paths
Use PyRIT when the exploit depends on conversation history, transformations, multimodal input or a sequence of tool calls. Give the campaign explicit boundaries: approved targets, maximum turns, allowed tools, test accounts and a stop condition when a real secret or destructive action is approached.
4. Test the full RAG or agent scenario
Model-only tests cannot show whether an untrusted document can override a system instruction, whether retrieved data crosses a tenant boundary or whether an agent can call a tool with excessive authority. Build Giskard or DeepTeam scenarios that include retrieval, memory, tool permissions and the final side effect. Assert both the model response and the application action.
5. Review the software around the model
Run ordinary source-code and dependency analysis, secret detection, configuration review and authorization tests. DOE guidance specifically warns that Promptfoo, PyRIT and garak are not source-code scanners or authorization systems. The same limitation applies to any red-team harness: it can expose observed behavior, but it does not prove that every code path or permission boundary is safe.
6. Add production safeguards
Use least-privilege service accounts, explicit tool allowlists, tenant-aware access checks, logging and rate limits. Add runtime detection or guardrails where the impact of a successful prompt injection is high. Testing finds weaknesses before or during operation; controls reduce the damage when an unknown weakness is encountered.
Interpreting findings without overclaiming
Confirm the behavior
Replay the exact input, conversation state, model version and application configuration. A finding that disappears after a model update may still matter if customers can select the older model or if a different route retains the vulnerable behavior.
Separate model behavior from application impact
An unsafe completion is not automatically a security incident. Determine whether the output exposed confidential data, changed an authorization decision, invoked a tool, altered durable state or merely violated a content policy. Severity should reflect the reachable impact and required attacker access.
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Keep the reproducer, relevant prompts, tool-call transcript, response, timestamps and commit or configuration identifiers. Redact real credentials and personal data before sharing a report. Candidate patches and automated scores are starting points for review, not proof that a fix is complete.
Understand false negatives
Probe catalogs and campaign logic cover only the inputs and evaluators you run. A passing result can miss a different language, a longer conversation, a new retrieval document, a changed tool schema or an authorization flaw that the model never exposes during testing. Record coverage boundaries with every report.
Quick Recap
Common mistakes when replacing Anthropic OSS Scanner
- Treating eligibility as a product feature: Anthropic’s scanner is selective and opt-in. A self-serve tool is necessary when a project is not accepted or needs testing between periodic scans.
- Calling a red-team harness a code scanner: Endpoint behavior does not reveal every vulnerable dependency, unsafe parser or missing permission check.
- Using one-shot prompts against a stateful agent: Multi-turn escalation, retrieval poisoning and tool misuse often require a campaign that preserves state.
- Ignoring data handling: Check whether prompts, responses, source code and model artifacts leave your environment before using a hosted platform.
- Failing to pin test context: Without model, prompt, corpus and commit metadata, you cannot tell whether a result changed because of your fix or because the test target changed.
- Equating a generated patch with remediation: Review, test and deploy any suggested fix through the same process as human-written code.
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