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The strongest practical shortlist is Promptfoo for configuration-driven evaluations and red-team testing, Microsoft PyRIT for scripted multi-turn attack campaigns, NVIDIA garak for probe-based scanning, and Inspect for structured evaluations built from datasets, agents, tools, and scorers. They address different testing jobs, so choose according to your agent and workflow rather than treating one as a complete security solution. Testing can expose failures; it does not authorize actions, limit tool access, or certify an agent as secure.
What should you look for in an open-source AI agent testing tool?
Choose a tool by what it can exercise in your system, how you can define and repeat tests, and whether your team can safely handle its test data and results. A scan of a model’s answers is not enough when the deployed agent can retrieve private information, call tools, or change application state.
OWASP’s DevSecOps guidance captures the central risk: “A safe base model wired to an over-privileged tool is still exploitable.” Test the complete application path—not only the underlying model or its response to a prompt. That means accounting for retrieval, indirect inputs, tool permissions, and the effects of actions on state.
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- Test style: Does your team need declarative evaluations, scripted conversations, probes, or a composable evaluation framework?
- Repeatability and scoring: Can you define what counts as a failure and rerun confirmed failures as regression tests?
- Workflow fit: Can the team maintain the tests and integrate them into its development process?
- Data handling: Where do prompts, results, and generated attacks go? Review the tool’s settings and any hosted services before using sensitive data.
Also distinguish an open-source project from a guarantee about a particular deployment. Promptfoo’s repository identifies it as open source under the MIT license, but that does not make every hosted feature, integration, or data-handling choice suitable for every environment. Check current project documentation for the configuration you plan to run. Features, ownership, license terms, command-line behavior, and data practices can change.
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The U.S. Department of Energy’s GEAR guidance puts the boundary plainly: “No single harness covers every attack.” A testing tool is one part of a security program, not a substitute for access controls or operational safeguards. The practical question is not simply which tool has the longest feature list; it is which one can help your team find meaningful failures in the system it actually operates.
Which open-source tools are worth considering?
Promptfoo, PyRIT, garak, and Inspect are a useful shortlist, but the evidence supports matching them to distinct jobs—not ranking them by a universal measure of security effectiveness. Their documented approaches differ, and you should verify current project documentation for the configuration, target support, and data practices you intend to use.
| Tool | Best-supported use | What to assess before choosing |
|---|---|---|
| Promptfoo | Configuration-driven evaluations, red-team tests, vulnerability scanning, and CI/CD workflows for LLM applications and agents. Its repository identifies the project as open source under the MIT license. | Check current configuration and CLI behavior, target coverage, and data handling—including any hosted generation or telemetry settings relevant to your environment. |
| Microsoft PyRIT | Scripted, multi-turn generative-AI attack campaigns. | It is an orchestration framework. Consider whether your team can design campaigns and support the Python integration they require. |
| NVIDIA garak | Probe-based scanning using probes and detectors to look for model or application weaknesses. | Verify that your target is supported and that model-oriented probes address the agent’s actual tool-use risks. |
| Inspect | Structured evaluations composed from datasets, agents, tools, and scorers; it supports tool calling and arbitrary external agents. | Assess whether its scoring and evaluation workflow fits your use case and infrastructure. |
Compare candidates along several dimensions rather than reducing the decision to a single score: which part of the system each can reach, how tests are authored, whether multi-turn interaction or tool use is represented, how results are scored, how easily cases can be repeated in CI, where test data travels, and how much specialist effort it takes to maintain a useful suite. A tool that finds a model-level weakness may still leave a gap around permissions; a framework that supports arbitrary agents may still need carefully designed cases and scorers.
When does Promptfoo fit?
Consider Promptfoo when you want to describe evaluations in configuration and use red-team testing or vulnerability scanning in a repeatable development workflow. The project’s repository identifies it as MIT-licensed and open source; that licensing fact does not establish that every hosted feature, integration, or data-handling choice is appropriate for your environment. Check the current documentation and settings before connecting sensitive prompts or results to external services.
Promptfoo’s live documentation lists 157 plugins across six categories. Treat that as a vendor-maintained feature count, not an independent finding about effectiveness, attack coverage, or the percentage of your agent’s risks that the tool can detect. Confirm what the current plugins test and whether those tests map to your application’s tools, data, and actions.
When does PyRIT fit?
PyRIT is the clearest fit in this shortlist for orchestrating scripted, multi-turn attack campaigns. That approach can suit teams that want to develop a sequence of interactions rather than rely only on isolated prompts. Plan for campaign design and Python integration: choosing a framework does not automatically supply a complete campaign tailored to your agent.
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For an agent, campaign design should account for the fact that behavior can emerge across several turns and connected components. Decide what the campaign is meant to probe, what a meaningful failure looks like, and which parts of the application it can safely reach. The framework’s multi-turn orientation is not a promise that a given campaign covers every path or that an attack will be found without careful interpretation of results.
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When does garak fit?
garak’s probe-and-detector approach makes it a candidate for scanning supported models or applications for weaknesses. Confirm that it can exercise the target you actually operate. A probe against a model does not, by itself, show whether an agent’s connected tools have appropriate permissions or whether a state-changing action is safe.
Use the distinction between a probe result and an application security conclusion carefully. A scanner may help surface a weakness in the component it can reach, but the agent’s retrieval path, permission checks, tool behavior, and resulting state need to be evaluated in context. Verify target support and decide whether your risk cases require a broader application-level harness or separate controls.
When does Inspect fit?
Inspect is designed for structured evaluations assembled from datasets, agents, tools, and scorers. Its support for tool calling and arbitrary external agents may be relevant when you need evaluations around a connected agent rather than only a standalone model. The important choice is whether its evaluation and scoring workflow matches your infrastructure and the risks you need to assess.
Think through the full evaluation design: the dataset represents the cases, the agent and tools represent the system being exercised, and the scorer determines how outcomes are judged. A structured setup can make an evaluation easier to reason about, but the quality of the result still depends on whether the cases and scoring criteria represent the risks that matter. Validate the workflow against your real target rather than assuming that composability means automatic coverage.
How do you test an AI agent without mistaking evaluation for security?
Start with risks and expected behavior for each data source, tool, and state-changing action. Then test the whole path that could produce a failure, set risk-specific pass/fail criteria, and preserve confirmed failures so changes do not quietly reintroduce them.
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- List the important assets and actions. Identify sensitive data, retrieval sources, connected tools, permissions, and actions that change state. For each, write down what the agent should and should not be able to do.
- Define concrete failure cases. Turn those risks into testable expectations—for example, whether the agent discloses information it should not access or performs an action without the required authorization. Treat these as cases to validate against your own system, not as guaranteed coverage from a tool.
- Exercise the application path. Include retrieval, indirect inputs, tool calls, permission checks, and resulting state changes where they are part of the system. Do not treat instructions in a prompt as an access-control boundary.
- Set thresholds by risk. Define what constitutes a pass for each case. A severe data leak or unauthorized action should not be obscured by a high average pass rate across less consequential tests.
- Keep confirmed failures as regression cases. Rerun them after changes that can alter behavior, including changes to prompts, models, tools, retrieval sources, or guardrails.
- Review the test’s own data flow. Inspect generated attacks and results, use approved test data and endpoints, and determine whether any content is sent to a hosted or other external service.
- Pair evaluation with security controls. Keep authorization, least-privilege tool access, dependency checks, secret protection, operational monitoring, and human review in place. A passing evaluation cannot establish that these controls exist or are effective.
This process makes test results more useful by tying them to defined risks and repeatable cases. It still cannot prove that every attack path has been found. Nor does a tool’s passing result grant permission to access a data source or perform an action. Authorization must be enforced by the application and its controls, not inferred from prompt instructions or from an evaluation report.
Make the test suite proportionate to the risks. A low-impact response-quality case and a severe unauthorized action should not have the same consequence in your reporting simply because each counts as one test. Define failure criteria before interpreting aggregate results, retain specific confirmed failures, and rerun them when a prompt, model, tool, retrieval source, or guardrail changes. A high overall pass rate cannot cancel out one serious failure.
Testing also has its own operational boundaries. Use endpoints and data approved for the exercise, inspect generated attacks rather than assuming they are all appropriate or meaningful, and check whether a hosted generation or telemetry setting sends test content outside your environment. Open-source status alone does not answer those questions. Review the actual configuration and data flow you intend to use.
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For agents that answer from documents, check not only whether an answer sounds plausible but whether its claims are supported by the sources and represent them adequately. NIST describes evaluation probes that compare agent claims with a human-curated reference corpus and produce a machine-readable audit trail.
- Faithfulness: Does a source support the claim?
- Completeness: Is the source’s message represented fully?
- Sufficiency: Does the evidence support the claim being made?
These dimensions help assess attribution and evidence quality. They do not establish that the agent was authorized to retrieve the source, call a tool, or take an action; those need separate checks. A trace or audit record can make it easier to inspect what evidence supported a claim, but evidence quality and permission are different questions. Include both when the agent can access sensitive material or trigger actions based on its answers.
What should you know about coverage claims and test data?
Counts and successful test runs need careful interpretation. Promptfoo’s live documentation lists 157 plugins across six categories, but the year for that vendor-maintained feature count is not stated. A plugin count describes a set of features; it is not an independent effectiveness result or a measure of the proportion of attacks your agent is protected against.
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No independent comparative effectiveness, vulnerability-detection, or agent-security performance statistic is established for these four tools here. That is why a universal ranking or detection-rate claim would be misleading. Evaluate the cases that matter to your system instead.
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Generated attacks and evaluation content can also create data-handling risks of their own. Use approved endpoints and data, inspect what a tool sends outside your environment, and review generated cases before treating their results as meaningful. The U.S. Department of Energy’s GEAR guidance says tools support review; they do not replace data approval, access control, scientific testing, or human judgment.
Be cautious with both positive and negative results. A passing run means the tested cases passed under the conditions and scoring criteria used; it does not show that untested attacks are impossible. A failure is a prompt to investigate the actual application path and determine whether it reflects a security issue, a test setup problem, or a scoring mismatch. Keep confirmed bypasses as regression cases so that they remain visible after changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you choose among the four?
Begin with the system you need to test and the work your team can sustain. If you need configuration-driven evaluations and a red-team workflow, assess Promptfoo. If you need to orchestrate multi-turn campaigns, assess PyRIT. If probe-based scanning suits your target, assess garak. If you want evaluations composed from datasets, agents, tools, and scorers, assess Inspect.
For any candidate, verify target support and integrations against your real deployment, not a simplified model-only setup. Run a small set of risk-based cases first, examine failures and data flows, and decide whether your team can maintain useful tests over time. These tools can complement one another, but using more tools does not automatically close gaps in authorization, permissions, monitoring, or human oversight.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsA sensible selection process is to write down a few high-consequence risks, then check each candidate against the actual agent path those risks involve. Ask whether the tool can exercise retrieval and tools, whether the test style fits the behavior you need to evaluate, how results are scored, whether cases can be repeated in your development process, and what data leaves your environment. Finally, consider who will author, review, and maintain the cases. The best fit is the tool your team can use to produce relevant, repeatable evidence—not necessarily the one with the broadest-sounding feature list.
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Frequently Asked Questions
What does Promptfoo’s 157-plugin count tell me?
It is a vendor-maintained count across six categories in Promptfoo’s live documentation; the year is not stated. It is not an independently measured security score, detection rate, or guarantee that a particular agent is covered.
Does open source mean an evaluation tool keeps all test data on my machine?
Not necessarily. A project’s open-source status does not establish where every integration, hosted service, or telemetry feature sends data; inspect the current settings and data flow for the configuration you plan to use.
Can an agent’s audit trail prove that a tool call was authorized?
No. An audit trail can record evaluation evidence or results, but authorization depends on the system’s permissions and controls. Test and review those controls independently.
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No. These tools can help reveal failures in the cases they exercise, but no single harness covers every attack. Testing does not replace authorization, least-privilege tool access, dependency checks, secret protection, monitoring, or human review.
Should I test only the underlying model?
No. Include the deployed application path: retrieval, indirect inputs, tool permissions, and effects on state. A safe base model connected to an over-privileged tool may still be exploitable.
How often should I rerun agent security evaluations?
Rerun repeatable checks after changes that can alter behavior, including changes to prompts, models, tools, retrieval sources, or guardrails. Keep confirmed failures as regression cases.
Which tool is best for a multi-turn attack campaign?
Microsoft PyRIT is the clearest fit in this shortlist for scripted, multi-turn generative-AI attack campaigns. Your team still needs to design campaigns and support the Python integration.
Do NIST’s evidence checks test whether an agent is allowed to use a source?
No. Faithfulness, completeness, and sufficiency assess whether claims are supported and represent evidence adequately. They do not establish retrieval authorization or whether an action is permitted.
The Bottom Line
Promptfoo, PyRIT, garak, and Inspect offer different ways to evaluate AI systems, not a universal security ranking. Choose based on target coverage, test style, repeatability, data handling, and the team’s ability to maintain cases. Test the complete agent application, preserve confirmed failures, and keep authorization and other security controls separate from evaluation.
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