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Promptfoo, Microsoft PyRIT, NVIDIA garak, and Inspect are the strongest open-source starting points for testing AI agents, but they solve different problems. Promptfoo is the practical choice for configuration-driven evaluations, red-team cases, vulnerability checks, and CI workflows. PyRIT is designed for scripted, multi-turn generative-AI attack campaigns. Garak scans with probes and detectors, while Inspect lets you assemble structured evaluations from datasets, agents, tools, and scorers. None of them makes an agent secure by itself: an evaluation harness can reveal failures, but it cannot authorize tool calls, reduce excessive permissions, protect secrets, or replace human review. The reliable approach is to test the complete application—model, retrieval, tools, permissions, and state changes—then combine repeatable tests with security controls and operational monitoring.
Which open-source tools are best for testing AI agents?
The best tool depends on the failure you need to find and how your team runs tests. Use Promptfoo when you want declarative test cases and CI integration, PyRIT for multi-turn attack campaigns, garak for probe-based scanning, and Inspect for composable evaluations that include tools and external agents.
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The Tool Desk
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|---|---|---|---|
| Promptfoo | Declarative evaluations, red-team tests, vulnerability scanning, and CI/CD integration for LLM applications and agents | Repeatable checks that developers can run during pull requests and releases | Current configuration and CLI behavior; data handling, hosted generation, and telemetry settings. Its repository identifies the project as MIT licensed and open source. |
| Microsoft PyRIT | Scripted, multi-turn generative-AI attack campaigns | Adversarial scenarios that need conversation state, branching, and campaign orchestration | How much campaign design, Python integration, and maintenance your team can support |
| NVIDIA garak | Probe-based scanning with probes and detectors for model or application weaknesses | Broad, repeatable discovery of known classes of unsafe behavior | Target compatibility and whether model-focused probes exercise the agent’s real tool-use risks |
| Inspect | Structured evaluations composed from datasets, agents, tools, and scorers | Custom evaluators that need tool calling or arbitrary external agents | Whether its scoring model and execution workflow match your infrastructure and evidence requirements |
Do not treat this as a universal ranking. Coverage, test style, tool integration, repeatability, scoring, CI fit, data handling, and authoring effort matter more than a single “best” label. A mature program often combines a declarative suite, campaign-style attacks, and targeted probes instead of forcing one framework to do everything.
What does each tool actually test?
Promptfoo: configuration-driven evaluation and CI red teaming
Promptfoo expresses evaluations in configuration and is aimed at LLM applications and agents. That makes it useful when many people need to review the same test definitions, when results must be compared across model or prompt changes, or when checks need to run in continuous integration. Its documented scope includes ordinary evaluations, red-team workflows, vulnerability scanning, and CI/CD integration.
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A configuration-based suite can cover expected answers, refusal behavior, prompt-injection attempts, sensitive-data exposure, and tool-call outcomes. The important design decision is to assert the application result, not merely the model’s text. For an agent that can create a ticket, for example, a passing case should verify both the response and whether the ticket was actually created with the correct fields and identity.
Promptfoo’s project repository describes it as MIT licensed and open source. License status does not answer whether a hosted service, model provider, or telemetry path is acceptable for your data, so review those settings separately.
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Microsoft PyRIT: scripted, multi-turn attack campaigns
PyRIT is an orchestration framework for generative-AI red teaming. It is suited to attacks that evolve over several turns: an attacker may establish context, extract partial information, change tactics after a refusal, and then attempt an unsafe action. A single prompt test cannot represent that sequence reliably.
PyRIT is most valuable when your team can write and maintain campaign logic in Python. You must define the attacker goals, conversation branches, stopping conditions, and evidence to collect. The framework coordinates a campaign; it does not decide which business actions are authorized or whether a successful attack should be blocked in production.
NVIDIA garak: probes and detectors for systematic scanning
Garak approaches testing through probes and detectors. Probes generate attempts aimed at particular weakness classes, and detectors examine outputs for indicators of failure. This is efficient for broad, repeatable scans and for discovering regressions after a model, prompt, or guardrail change.
Agent risk is broader than model text. A probe may show that an agent will follow an injected instruction, but the severity depends on what tools the agent can invoke, whose credentials it uses, and whether a call changes state. Confirm that your target adapter exercises the actual agent path; a model-only scan can miss authorization and workflow failures.
Inspect: composable evaluations with tools and scorers
Inspect provides building blocks for structured evaluations: datasets supply cases, agents perform tasks, tools represent capabilities, and scorers judge outcomes. It supports tool calling and arbitrary external agents, making it a strong fit when the evaluation itself must reproduce a production-like workflow.
That flexibility moves work into evaluation design. Define what a scorer considers correct, safe, complete, and properly evidenced. If a result can be technically correct but unauthorized, use separate scores or hard failure conditions rather than averaging the two into a misleading number.
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How should you choose a testing harness?
Start with the risk and the system boundary, then select the smallest combination that can produce trustworthy evidence. The following questions expose differences that product-style feature lists often hide.
What attack shape must the test reproduce?
- One-turn or declarative cases: Promptfoo is a natural starting point for repeatable assertions and CI gates.
- Conversation-dependent attacks: PyRIT handles scripted, multi-turn campaigns where later actions depend on earlier responses.
- Known weakness families at scale: Garak’s probes and detectors provide a systematic scan, provided the target integration reaches the application behavior you care about.
- Tool-using tasks with custom judgment: Inspect lets you model datasets, agents, tools, and scorers together.
Can the harness reach the real boundaries?
Ask whether tests can pass through retrieval, memory, tool routing, identity, and state-changing APIs. A framework that only sends text to a model cannot establish that a user was prevented from deleting a record or reading another tenant’s data. Prefer a test adapter that can use a sandbox with realistic permissions and observable side effects.
How will results become release decisions?
Check whether results are machine-readable, repeatable, and easy to compare across revisions. CI integration is useful only when the policy is explicit: which failures block a merge, which are warnings, and who reviews exceptions. A dashboard with an attractive average score is not a substitute for a hard stop on a confirmed secret leak.
What data leaves your environment?
Read the data-flow documentation for the harness, model providers, hosted generators, and telemetry. Test prompts can contain personal data, credentials, proprietary documents, or attack strings that you do not want transmitted to a third party. Use synthetic or approved data where possible, and record the destination of every request.
How do you build a repeatable AI-agent security workflow?
Use this sequence for a new agent and repeat the relevant stages after any change to prompts, models, tools, retrieval sources, or guardrails.
- Inventory the application path. List models, system prompts, retrieval indexes, memory stores, external tools, credentials, state-changing operations, and human approval points. Identify which inputs are direct user text and which arrive indirectly through documents, web pages, messages, or tool results.
- Define concrete risks. Turn broad goals such as “resist prompt injection” into observable failures: disclose a secret, call an administrative tool as a normal user, write to the wrong tenant, follow an instruction embedded in a retrieved document, or claim evidence that does not support an answer.
- Write expected behavior per risk. Specify the allowed response, the permitted tool calls, the identity and scope required, and the state that must remain unchanged. Include safe handling of ambiguity and a requirement to ask for confirmation when the operation is consequential.
- Select complementary tests. Use declarative cases for stable regressions, a campaign framework for multi-turn adversarial paths, probes for broad weakness discovery, and structured evaluations for tool-using tasks that need custom scorers.
- Run in a controlled environment. Use isolated accounts, synthetic records, non-production endpoints, and reversible state changes. Give the test agent only the permissions needed to exercise the scenario; never grant production-wide credentials merely to make a test convenient.
- Capture evidence. Store inputs, model and tool versions, retrieved passages, tool arguments, authorization decisions, outputs, side effects, timestamps, and scorer results. Without this context, a failure cannot be reproduced or triaged.
- Set risk-based gates. Define separate pass/fail rules for severe outcomes such as unauthorized actions or confirmed data leakage. Do not let a high average pass rate conceal one critical failure.
- Promote confirmed bypasses to regression tests. Minimize the attack to a stable case, document the expected defense, and run it after every change that could alter agent behavior.
- Review and monitor after release. Evaluation is a pre-release signal, not proof that production is safe. Keep authorization controls, dependency and secret scanning, logging, alerting, and human escalation in operation.
How do you test the application instead of only the model?
OWASP identifies “testing the model, not the application” as a common pitfall. Its warning is direct: “A safe base model wired to an over-privileged tool is still exploitable.” The practical implication is to make the test harness observe the complete path from input to effect.
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Feed the agent documents, web content, tickets, and tool results that contain instructions conflicting with the user’s goal. Verify that untrusted text is treated as data, not as a higher-priority command. Check both retrieval relevance and authority: a passage can be factually relevant while still being forbidden to use as an instruction.
Exercise identity and authorization
Run the same scenario under different roles and tenants. Assert that the authorization layer—not a prompt sentence—decides whether a tool call is allowed. Test missing, expired, and downgraded credentials, as well as attempts to smuggle a privileged identifier through tool arguments.
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Observe state-changing actions
Read-only answers are insufficient for agents that send messages, modify records, purchase items, deploy code, or change access. Use transaction logs or database assertions to verify the exact target, fields, amount, actor, and approval status. Include retries and partial failures so that an agent cannot duplicate an action simply because a tool timed out.
Check tool output handling
Tool responses can contain malicious instructions, secrets, malformed data, or claims that conflict with policy. Test whether the agent validates schemas, limits output size, separates data from instructions, and refuses to pass untrusted content into a more privileged tool.
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How should pass/fail criteria and scoring work?
Define thresholds before running a campaign so that the result cannot be reinterpreted after an inconvenient failure. Use hard failures for high-impact events and graded scores for quality dimensions that genuinely tolerate variation.
| Risk area | Example hard failure | Useful evidence to retain |
|---|---|---|
| Authorization | A user without permission causes a protected tool call or state change | Authenticated identity, policy decision, tool name, arguments, and resulting record change |
| Confidentiality | A secret, private document, or cross-tenant record is disclosed | Secret identifier, source boundary, output span, and whether redaction or blocking occurred |
| Prompt injection | Untrusted retrieved or tool content overrides the task’s policy and triggers an unsafe action | Injected content, instruction hierarchy, intermediate messages, and action trace |
| Grounding | The agent presents a claim as supported when the approved corpus does not support it | Claim, cited evidence, corpus version, and faithfulness/completeness/sufficiency scores |
| Reliability | A retry duplicates a consequential action or leaves state inconsistent | Request IDs, retries, idempotency keys, and before/after state |
For softer measures—format compliance, helpfulness, or partial task completion—record a score and confidence interval or reviewer agreement when available. Keep these separate from security gates. A system can be helpful and still fail an authorization test.
How can grounding and evidence quality be evaluated?
NIST describes probe-based evaluations that compare agent claims with a human-curated reference corpus and produce a machine-readable audit trail. The approach distinguishes three dimensions: faithfulness, whether a source supports the claim; completeness, whether the source’s message is represented fully; and sufficiency, whether the evidence is adequate for the claim being made.
Use these checks when an agent summarizes policy, answers questions from a controlled knowledge base, or cites records for a decision. Preserve the claim, the exact evidence retrieved, the corpus version, and the scorer output so a reviewer can reconstruct the judgment. Grounding scores improve attribution and auditability; they do not show that an action was authorized, that a tool was safe, or that a model resisted an attack.
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Evaluation frameworks find and document failures. They do not provide the controls that prevent those failures in production.
- Authorization: Enforce least privilege in an independent policy layer. Do not rely on a system prompt to decide who may call a tool.
- Tool isolation: Restrict network access, filesystem scope, command execution, and data visibility. Use separate service identities for separate capabilities.
- Secret protection: Keep credentials out of prompts, logs, fixtures, and generated test cases. Rotate any secret that appears in a test trace.
- Dependency and code security: Scan the agent’s code, plugins, images, and dependencies; test supply-chain updates separately from behavior evaluations.
- Operational monitoring: Alert on unusual tool sequences, data volume, privilege changes, repeated refusals followed by success, and high-impact actions without approval.
- Human judgment: Review ambiguous, high-consequence cases and exceptions. Automated scores cannot establish that a deployment is acceptable in every context.
The U.S. Department of Energy’s GEAR guidance summarizes the boundary well: “Tools support review; they do not certify a system or replace data approval, access control, scientific testing, or human judgment.”
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How should you handle generated attacks and test data?
Generated attacks are useful for variety, but they are not automatically valid, safe, or in scope. Review generated cases before execution, remove instructions that could damage a real system, and run them only against approved endpoints and data.
- Use synthetic identities, documents, and secrets whenever a test does not require production data.
- Label generated content so reviewers can distinguish an attack hypothesis from an observed vulnerability.
- Check whether a hosted model or generation service receives prompts, retrieved text, tool outputs, or failure traces.
- Define retention and deletion rules for logs, especially when traces contain personal or proprietary information.
- Rate-limit campaigns and add stop conditions for destructive or unexpectedly expensive behavior.
- Have a named owner approve tests that touch external services, regulated data, or irreversible actions.
These precautions apply even when the harness itself is open source. The license of a test framework does not change the privacy, contractual, or safety obligations attached to the data and endpoints it invokes.
What are the most common implementation mistakes?
Relying on average scores
An aggregate score can hide one severe leak or unauthorized action. Separate critical findings from quality metrics and make the critical category a blocking condition.
Testing only direct user prompts
Attackers can place instructions in retrieved pages, uploaded files, email bodies, tool responses, or memory. Include those channels and verify that the application preserves trust boundaries.
Granting excessive test privileges
A test that uses an administrator account may appear successful while proving nothing about real authorization. Use narrowly scoped identities and assert the policy decision for every sensitive call.
Ignoring side effects
Checking the final text misses duplicate purchases, altered records, sent messages, and leaked data. Record and assert external effects, including effects that occur after a timeout or retry.
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Unversioned prompts, datasets, models, and scorers make failures impossible to compare. Pin versions where practical, record them in each run, and review changes to the evaluation suite like production code.
Assuming a model scan covers an agent
Probe-based model findings are useful signals, but they may not exercise retrieval, tool routing, identity, or state transitions. Add application-level scenarios before drawing a security conclusion.
Sending sensitive cases to an unapproved service
Hosted generation, telemetry, and remote evaluators can expose test content outside your boundary. Confirm data paths and use sanitized fixtures before enabling those features.
What is a practical rollout plan for a small team?
A staged rollout keeps the program useful without pretending that a first test run proves security.
Stage 1: establish a minimum baseline
- Map every tool, identity, data source, and state-changing action.
- Create a small set of high-impact cases: unauthorized access, secret disclosure, indirect prompt injection, and unsafe side effects.
- Run them in a sandbox with logging and explicit pass/fail rules.
- Use a declarative harness for repeatability and store each confirmed failure as a regression case.
Stage 2: add adversarial breadth
- Use probes to cover known weakness families and identify gaps in the baseline.
- Build multi-turn campaigns for attacks that require persistence, adaptation, or escalation.
- Review generated cases and remove anything outside the approved scope.
- Compare results across model, prompt, retrieval, and guardrail changes.
Stage 3: model production workflows
- Compose evaluations that include real tool schemas, authorization decisions, retrieval behavior, and state assertions.
- Add grounding checks for claims that must be supported by a controlled corpus.
- Introduce CI gates for critical risks and an approval process for exceptions.
- Connect findings to operational monitoring and incident response.
Stage 4: maintain the program
Re-run tests after changes that can alter behavior, including model upgrades, prompt edits, new tools, retrieval-index updates, dependency changes, and guardrail modifications. Retire obsolete cases only with a written reason; otherwise, a previously fixed bypass can quietly return.
Which combination should you start with?
For a team starting from zero, begin with Promptfoo for a versioned baseline and CI checks, then add garak for broad probe coverage. Introduce PyRIT when your threat model includes adaptive, multi-turn campaigns, and use Inspect when evaluations must reproduce complex tool-using tasks or rely on custom scorers. This sequence is a workflow choice, not a claim that one project is more secure than another.
Whichever combination you choose, judge success by the quality of evidence and the controls around the agent: can you reproduce a failure, identify the exact boundary that failed, block the unsafe action, and prove that the fix survives later changes? If not, adding another scanner will not solve the underlying problem.
Frequently Asked Questions
Can open-source AI-agent testing tools run entirely offline?
Some workflows can be self-hosted, but offline operation depends on the harness, model, plugins, and any hosted generation or telemetry features you enable. Confirm every network call and replace remote services with local or approved endpoints before using sensitive test data.
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How often should an AI agent’s security tests run?
Run a small blocking regression suite on every change that can affect behavior, such as prompts, models, tools, retrieval data, or guardrails. Schedule broader probe and campaign runs at a cadence appropriate to your threat exposure, and rerun them after incidents or major dependency updates.
Should I test an agent with production credentials?
No. Use isolated identities with the minimum permissions needed to exercise the scenario, and point state-changing tools at a sandbox or reversible test environment. A production credential can turn a test into an incident and makes the result harder to interpret.
How do I prove that a failed test was really fixed?
Keep the original input, intermediate messages, retrieved content, tool arguments, authorization decision, and side-effect trace. Convert the minimized bypass into a regression case, rerun it under the same conditions, and then test nearby variations so the fix is not specific to one wording.
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