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Choose the testing workflow that matches your risk question. Garak is an open-source probe scanner for finding model and LLM-system failures. Microsoft PyRIT is an open-source framework that packages configurable red-team scenarios and saved results into repeatable campaigns. Vijil’s Diamond Adaptive Red Team for Agents (DART), announced October 7, 2026, is a commercial service that Vijil describes as adaptive, multi-turn testing of agents, including their tool use and memory.
These are overlapping but not interchangeable approaches. The available material supports comparing their test surfaces, integration models, repeatability and agent interaction—not declaring a universally superior product. Use Garak for broad probe-driven discovery, PyRIT for configurable and repeatable scenario work, and DART when you need vendor-described adaptive attacks against an agent operating in its real environment.
What each approach is designed to test
Garak: probe-based vulnerability scanning
Garak is an open-source LLM vulnerability scanner. Its documented workflow runs a collection of probes against an LLM or an LLM-based system, then reports the prompt and response associated with a detected failure. The project describes its probe collection as community-maintained and growing over time.
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PyRIT: a framework for risk-identification campaigns
PyRIT (Python Risk Identification Tool for generative AI) is Microsoft’s open-source framework for proactively identifying risks in generative-AI systems. Its scanner reference lists scenario families for adaptive tests, Microsoft AI Red Team playbooks, benchmarks, Foundry and Garak-inspired tests. The latest scanner reference identified for this comparison is version 1.1.0; scenario names and capabilities can change as the project evolves.
Microsoft describes a scenario as a pre-packaged red-teaming playbook: a repeatable command that runs a defined test and produces a result that teams can save, share and compare. That packaging is useful when a security team needs a campaign that can be rerun after a model, prompt, tool permission or policy change. It does not guarantee that two organizations will obtain identical findings, because target behavior, model versions, defenses and configuration still affect results.
Vijil DART: adaptive red teaming for agents
Vijil announced Diamond Adaptive Red Teaming for Agents (DART) on October 7, 2026. In that announcement, Vijil describes a commercial system that uses multiple adversarial agents and adapts tactics across turns, episodes and engagements. The stated target is an agent running in its own environment, with testing that can exercise tool use, memory and multi-turn behavior.
This is a different emphasis from a fixed probe or a packaged scenario. An adaptive campaign can change its next action after seeing how the target responds, which is relevant when a weakness appears only after a conversation develops or after the agent invokes a tool. DART’s scope and performance statements in the launch material are vendor descriptions, not independent validation.
Vijil founder and CEO Vin Sharma characterized the need this way: “Your custom AI agent that can access your confidential data and take consequential action on its own requires a comprehensive and customized approach to quality control.” That is a vendor executive’s position, not a neutral security standard.
Side-by-side comparison
| Decision axis | Garak | PyRIT | Vijil DART |
|---|---|---|---|
| Primary form | Open-source probe scanner | Open-source risk-identification and red-teaming framework | Commercial adaptive red-team offering |
| Core workflow | Run probes against a model or LLM-based system and inspect failing prompts and responses | Run configurable scenarios or playbooks and save the resulting findings | Vijil describes multi-agent, multi-turn attacks that adapt across episodes and engagements |
| Best-documented scope | Model and LLM-system failure modes such as injection, leakage, hallucination, misinformation, toxicity and jailbreaks | Scenario families spanning adaptive tests, Microsoft playbooks, benchmarks, Foundry and Garak-inspired attacks | Agent behavior in its environment, including tool use, memory and multi-turn interaction |
| Repeatability | Depends on the selected probes, target setup and evaluation behavior | Scenarios are designed as repeatable commands with saveable, shareable results | Adaptive behavior may vary by engagement; the announcement does not establish a standardized reproducibility protocol |
| Evidence available for this comparison | Official project documentation and repository descriptions | Microsoft documentation and a July 9, 2026 Microsoft AI Red Team article | Vijil’s October 7, 2026 announcement; product and benchmark claims are vendor-reported |
How the testing workflows differ
Running a Garak-style probe campaign
- Define the target boundary. Decide whether the target is a base model, a model behind an application interface or a larger LLM-based system. Record the model version, system prompt or policy layer, enabled tools and any rate limits.
- Select probes by failure mode. Start with the risks relevant to the interface: prompt injection and jailbreak probes for instruction bypass, leakage probes for confidential-data exposure, hallucination probes for unsupported answers, and toxicity or misinformation probes where those outcomes matter.
- Execute the probes in an isolated environment. Use test credentials and non-production data. If the target can call tools, restrict those tools to harmless fixtures rather than live business actions.
- Capture the complete evidence. Preserve the probe name, exact prompt, model response, evaluator result, model and application versions, and any system or developer instructions supplied during the run.
- Triage and reproduce. Re-run a failing probe under the same configuration, then vary one control at a time—such as a policy prompt or model version—to determine whether the failure is stable, intermittent or configuration-specific.
Garak’s documented output emphasis is the failing prompt and response. That makes it well suited to finding concrete examples that developers can inspect and use as regression cases.
Building a PyRIT scenario campaign
- Choose a scenario family. The scanner documentation separates adaptive tests, Microsoft AI Red Team playbooks, benchmarks, Foundry scenarios and Garak-inspired scenarios. Select the family that matches your target and reporting needs.
- Check scenario prerequisites. Some scenarios specify a dataset, attack technique or prompt-cap requirement. Do not assume every scenario runs with the same inputs, budget or default behavior.
- Configure the target and controls. Define the model or application endpoint, authentication, allowed content, conversation limits and any tool or retrieval components included in the test.
- Run the packaged command or playbook. Follow the scenario’s documented parameters rather than treating the entire catalog as one uniform scanner. Record the selected scenario, version and configuration.
- Save and share results. PyRIT’s scenario model is explicitly intended to produce results that teams can save, share and compare. Store the run metadata with the outputs so a later run can distinguish a code change from a changed test setup.
- Turn findings into regression coverage. Preserve successful attack conversations and failed safeguards as repeatable checks after prompt, model, retrieval or tool-permission changes.
PyRIT’s practical question is “where are we exposed?” Its value is not only the individual attack but the ability to organize many attacks into named, repeatable campaigns.
Planning an adaptive DART engagement
- Map the agent’s real operating environment. Document tools, APIs, memory stores, retrieval sources, user roles, approval gates and actions that could change external state.
- Define safe test identities and resources. Create non-production accounts, synthetic records and reversible tool actions. A multi-turn test can reach a consequential step even when its opening prompt looks harmless.
- Specify the behaviors to exercise. Include instruction following, tool selection, authorization boundaries, memory retention, cross-turn context, data access and escalation to a human reviewer.
- Set engagement limits. Establish allowed tools, maximum episodes, data-handling rules, stop conditions and monitoring contacts before testing begins.
- Review the full episode, not only the final answer. Preserve the adversarial messages, intermediate plans, tool calls, returned data, memory writes and final action. An agent can produce a polite final response after making an unsafe intermediate call.
- Retest after remediation. Adaptive attacks may find paths that are not represented by a single prompt. Re-run the engagement after changing permissions, memory handling, tool validation or approval logic.
These steps describe the controls needed for any agent-focused engagement. Vijil’s announcement specifically claims that DART adapts tactics across turns and tests tool use and memory; it does not publish a universal setup or a guarantee that every engagement exercises the same paths.
Coverage readers should expect from the documented catalogs
Garak’s probe-oriented coverage
Garak’s documented categories are organized around ways an LLM or LLM-based system can fail. Typical examples named by the project include:
- Prompt injection and jailbreak attempts that try to override instructions or safety controls.
- Data leakage, including responses that reveal information the system should not disclose.
- Hallucination and misinformation, where the system presents unsupported or false content.
- Toxicity generation and other harmful-language failures.
The exact probe set is community-maintained, so coverage can expand and individual probes can change. Treat the selected probe list and version as part of the test record.
PyRIT’s Garak-inspired scenarios
Microsoft’s scenario documentation names Garak-inspired tests for a wider set of input and output paths:
| Scenario area | What it is intended to explore | Important qualification |
|---|---|---|
| Prompt injection | Attempts to alter the system’s instructions or behavior through attacker-controlled content | Inputs, model defenses and limits are scenario-specific |
| Web injection | Injection delivered through web or retrieved content | Requires a target with the relevant web or retrieval path configured |
| Credential-like outputs | Whether the system emits strings resembling secrets or credentials | A detector result is not proof that a real credential was exposed |
| System-prompt extraction | Attempts to elicit hidden system or policy instructions | Success depends on the application’s prompt assembly and response filtering |
| Package hallucination | Whether the model invents software packages or dependencies | Useful for coding workflows; it does not replace dependency or supply-chain checks |
| Audio attacks | Risk paths involving audio inputs or outputs | Requires an audio-capable target and the scenario’s specified inputs |
| Visual jailbreaks | Jailbreak attempts delivered through images or other visual inputs | Requires multimodal capability and any stated dataset or prompt limits |
Some scenarios have explicit dataset, technique and prompt-cap requirements. Therefore, “PyRIT supports” a scenario does not mean it runs automatically in every installation or that all scenarios have the same cost and coverage.
DART’s claimed agent surface
Vijil’s launch material emphasizes behavior that ordinary single-turn model probes can miss: an adversary can continue across turns, observe the result of a tool call, exploit memory written earlier in the episode and change tactics when a defense blocks one route. The announcement says DART tests agents in their environment and includes tool use and memory. The available description does not provide a complete public attack catalog, so readers should ask for the engagement methodology, logging format, controls and reproducibility details before treating those claims as comparable to a named open-source scenario.
Repeatability, scoring and evidence quality
Security teams often use “repeatable” to mean several different things. Separate them:
- Input repeatability: the same prompts, datasets and attack techniques are used again.
- Execution repeatability: the same model, application version, permissions and limits are present.
- Outcome repeatability: the system produces the same pass or fail result.
- Comparison repeatability: results from two runs or teams can be compared without hidden configuration differences.
PyRIT explicitly supports the first two through scenario packaging and saveable results, but its documentation does not prove identical outcomes across environments. Garak can be highly repeatable when the probe set and target are pinned, yet model sampling, rate limits and evolving community probes can change results. An adaptive DART engagement may deliberately change its path in response to the agent, so a useful record must include the episode transcript and environment state rather than only a single score.
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For every tool, report at least:
- Target model and application version.
- System, developer and policy instructions in force.
- Enabled tools, retrieval sources and memory behavior.
- Attack or scenario name and version.
- Number of prompts, turns or episodes attempted.
- Definition of a failure and the evaluator used.
- Observed impact, affected data and whether an external action occurred.
- Evidence needed to reproduce the finding.
How to choose for common security questions
| Your primary question | Most natural starting point | Why | What to add |
|---|---|---|---|
| Can this model be induced to produce unsafe or unreliable outputs? | Garak | Its probe workflow is designed to scan model and LLM-system failure modes and show failing prompts and responses. | Add application-specific tests for retrieval, tools and authorization. |
| Where are we exposed across a managed generative-AI application? | PyRIT | Scenario families and saved playbooks support a structured campaign across multiple risk areas. | Pin scenario versions and document prerequisites such as datasets and prompt caps. |
| Can an attacker chain turns, memory and tools into an unsafe action? | An agent-focused adaptive engagement such as the DART scope described by Vijil | Vijil specifically describes adaptive, multi-turn testing in the agent’s environment. | Require clear boundaries, transcript-level evidence and independent review of vendor claims. |
| Do we need an open-source tool we can inspect and extend? | Garak or PyRIT | Both are described as open-source projects, with different abstractions: probes versus scenarios and framework components. | Budget engineering time for integration, maintenance and evaluator tuning. |
| Do we need a commercial service and customized engagement? | Evaluate DART against the agent’s operational requirements | It is presented as a commercial offering rather than a self-managed open-source scanner. | Request scope, data handling, reporting, retention, support and reproducibility terms. |
A practical layered test plan
Most teams do not have to choose only one layer. A defensible program can use the approaches for different questions:
- Baseline model behavior with probes. Run a pinned Garak probe set against the model or LLM endpoint. Capture representative failures and establish a regression baseline.
- Expand to application scenarios. Use PyRIT scenarios for prompt injection, web injection, system-prompt extraction, credential-like outputs, package hallucination and any audio or visual paths the application actually exposes.
- Exercise real agent workflows. Test authorization, tool invocation, memory and cross-turn behavior in a controlled environment. If you use DART or another adaptive service, require episode-level logs and explicit stop conditions.
- Classify impact. Separate a harmful text response from disclosure of protected data, unauthorized tool use or an irreversible external action. Severity should reflect the consequence, not merely the existence of an attack string.
- Remediate the control that failed. Possible controls include tool allowlists, argument validation, least-privilege identities, retrieval filtering, memory isolation, output validation and human approval for consequential actions.
- Re-run the same baseline. Keep the original probe and scenario versions so a pass after remediation means the same check was actually repeated.
- Add newly discovered paths to regression tests. An adaptive episode is valuable even when it cannot be reduced to one static prompt; preserve the sequence, tool state and preconditions needed to replay it.
Common pitfalls and how to avoid them
Confusing a model scan with an agent assessment
A prompt-injection failure in a model endpoint does not show that an agent will take an unauthorized action, and a clean model scan does not establish that the agent’s memory or tools are safe. Test the complete path when the risk depends on application state or external effects.
Assuming every catalog item runs by default
PyRIT’s Garak-inspired scenarios include different modalities and requirements. Verify the dataset, technique, prompt cap, target capability and evaluator before comparing counts between runs.
Reporting a score without the denominator
“Attack success rate” is meaningless without the number of attempts, attack definition, target configuration, evaluator and handling of partial successes. Store raw conversations and tool traces alongside aggregate metrics.
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Allowing tests to reach production actions
Use synthetic data and reversible tools. A test account with read-only permissions is safer than a production identity, particularly for adaptive multi-turn campaigns.
Treating vendor benchmarks as independent evidence
Vijil’s DART figures come from its own launch announcement. They should be labeled company-reported until an independent report provides the methodology, datasets, competitors, configurations and results.
What the published DART figures do—and do not—show
Vijil’s October 7, 2026 announcement reports two headline results:
| Reported figure | Source and qualification | How to use it |
|---|---|---|
| 1.5× attack success rate versus its closest competitor on the DecodingTrust-Agent benchmark | Vijil Inc., 2026; company-reported in the launch announcement and not independently validated here | Treat it as a vendor claim, not a generally established ranking |
| Nine of twelve enterprise agent tasks | Vijil Inc., 2026; reported in the same announcement and not independently validated here | Ask for task definitions, scoring rules, baselines and full results before using it for procurement |
No independent benchmark report was identified to verify those comparisons. The figures also do not establish that DART is better for every model scan, PyRIT scenario or Garak probe. They address the conditions and methodology used for the company’s announcement.
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- Can the target be tested without exposing real secrets or allowing irreversible actions?
- Will the tool record prompts, responses, tool calls, memory writes and retrieved content with timestamps?
- Can you pin the model, scenario or probe version and reproduce a finding later?
- What counts as a failure, and can your team inspect or change the evaluator?
- How are multimodal inputs, retrieval content and external tools represented in the report?
- For a commercial adaptive service, where is data processed, how long is it retained and what evidence is delivered after the engagement?
- Can findings be converted into regression tests that run after each model or policy change?
The Bottom Line
Garak is the clearest fit for open-source, probe-driven scanning of model and LLM-system failure modes. PyRIT is the stronger fit when you need configurable scenario campaigns with documented prerequisites and saveable results. DART is the relevant option when your central risk is an autonomous agent chaining turns, memory and tools; however, its scope and benchmark advantages remain descriptions and claims from Vijil’s October 2026 announcement. Select—and combine—the approaches according to the behavior you must test, then require reproducible evidence for every finding.
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