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Choose an open-source AI scanner by the boundary you need to test, not by the word “AI” in its name. garak is documented for probing language models and chatbot-style targets; Giskard’s open-source library is designed around agentic applications, including chatbots and retrieval-augmented generation (RAG) systems; and PyRIT is useful when you need to assemble and automate custom red-team scenarios. A scan provides sampled evidence under a particular configuration. It is not a security certificate, and a clean result does not prove that an application is safe.
Start with the system boundary
“AI vulnerability scanning” covers several different targets. A base model test can reveal jailbreaks or harmful responses, but it does not automatically exercise retrieval permissions, application authentication, data stores, or tool execution. A scanner that connects successfully to a model may therefore miss the controls that matter most in a production application.
| Target boundary | What a useful test must reach | Typical questions |
|---|---|---|
| Base model | Model endpoint or local model runtime | Does the model follow prohibited instructions, disclose system information, or produce unsafe content? |
| Chatbot or model-backed API | Conversation interface, authentication and request handling | Do prompts bypass policy, leak context, or trigger inconsistent behavior across turns? |
| RAG application | Retriever, indexed documents, prompt construction and answer path | Can hostile text in a document override instructions, expose unauthorized passages, or alter citations? |
| Tool-using agent | Planner, tools, permissions, side effects and approval gates | Can an instruction cause an unauthorized API call, data change, or exfiltration? |
Write the boundary down before selecting a package. A model-level probe run cannot establish that retrieval filters, authorization checks, tool permissions, logging, or storage controls are secure. Those components must be reachable through the test harness or assessed separately.
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Compare scanners on six practical axes
The following comparison reflects capabilities described in the projects’ documentation. It is a scope guide, not an effectiveness ranking; no independent, apples-to-apples performance statistic is established for these tools.
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| Axis | garak | Giskard open-source library | PyRIT and similar frameworks |
|---|---|---|---|
| Primary boundary | Language models and chatbot-style targets | Agentic applications, including chatbots and RAG systems | Custom red-team workflows assembled around a target |
| Attack coverage documented in the supplied material | Configurable probe families; exact coverage depends on selected probes and version | Prompt injection, direct adversarial prompts, multi-turn jailbreaks, suffix injection and harmful-prompt datasets | Scenario-driven testing; Microsoft’s documentation shows a document-carried latent-injection scenario derived from garak |
| Target integration | Local Hugging Face models and API-style integrations are documented target paths | Runs against an agent description and its interactions; provider and harness details are part of setup | Designed for orchestrating attacks against the interfaces you configure |
| External-model data handling | Depends on the target and generators you configure | The scan’s language-model provider receives the supplied agent description and conversation messages; it does not receive source code, secrets or tools unless those are included | Depends on the models, connectors and logging selected by your workflow |
| Repeatability | Repeatable when you pin probe, target and runtime configuration | Generated scans can be converted into explicit checks and replayed in saved suites and CI | Repeatability depends on the scenarios and harness you save |
| How findings are judged | Review generated reports and probe outcomes | An adversarial model generates scenarios and another model role judges replies; human review remains necessary | Review each scenario’s input, output and scoring logic |
What garak is suited to test
Documented purpose and targets
garak describes itself as an LLM vulnerability scanner for discovering weaknesses and unwanted behavior in language-model systems. Its documentation covers scanning a chatbot or model and producing a report. The NVIDIA repository documents selecting probe families and configuring targets, including local Hugging Face models and API-style integrations.
That makes garak a reasonable first candidate when your question is model-centric: “How does this model respond to adversarial prompts?” or “Does this chatbot endpoint exhibit known unwanted behaviors?” It is not evidence that the entire surrounding application has been exercised.
Probe selection is your coverage boundary
garak allows probe and target selection. A run that enables only a small subset of probes must be described as a narrow test, not broad AI security coverage. Record the probe families, target adapter, model version, generation settings and run date with the report. Probe names and supported generators can change, so verify them against the version you install; garak’s own documentation is marked as a work in progress.
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If the main risk is an agent making an unauthorized tool call, a model-only probe does not observe the permission boundary or side effect. If the risk is malicious content in retrieved documents, connect the scanner to a harness that performs retrieval and prompt assembly, or add a separate indirect-injection test. Do not infer application-level coverage from a successful model connection.
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What Giskard’s open-source scan adds
Application-oriented workflow
Giskard’s open-source Python library is documented for testing and scanning agentic applications, including chatbots and RAG systems. Its workflow distinguishes a generated vulnerability scan from explicit checks. The generated scan searches for unknown failures; explicit checks encode expected behavior so that a confirmed issue can be replayed after a code or model change.
Attack families in the documented guide
The vulnerability-scan guide specifies direct prompt injection, multi-turn jailbreak techniques, suffix injection, harmful-prompt datasets and other adversarial scenarios. These categories are useful only when the harness exposes the relevant application behavior. For example, a direct prompt sent to a chat endpoint cannot test whether a hostile instruction hidden in a retrieved document changes the answer unless retrieval is part of the exercised path.
Provider and privacy implications
Giskard states that its scan uses one language model to generate adversarial scenarios and another model role to judge replies. The provider receives the agent description and conversation messages supplied to the scan. According to the documentation, it does not see source code, secrets or tools unless you include them in that content. Inspect prompts, conversation transcripts, retrieved passages and custom metadata before running a scan, and choose a provider and deployment mode that meet your data requirements.
Turning discoveries into regression tests
Use the generated scan to discover a failure, then preserve the exact interaction as an explicit check. Save the check in a suite and replay it in continuous integration. This separates exploratory sampling from the deterministic tests that protect a known security property.
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PyRIT and the wider open-source landscape
OWASP AI Exchange lists PyRIT, garak, Prompt Fuzzer, Guardrail and Promptfoo among open-source generative-AI red-teaming tools. Treat that list as a map of projects to investigate, not as a benchmark or proof that they have equivalent scope. Their integrations, maintenance status, attack libraries and scoring behavior can differ substantially.
Why indirect prompt injection deserves a dedicated test
Microsoft’s PyRIT documentation includes a Garak-derived latent-injection scenario: a malicious instruction is planted inside a document that the model is asked to read. This models an indirect prompt injection, in which hostile content arrives through a retrieved file, web page or user-supplied attachment rather than the user’s direct message.
When evaluating any framework, ask whether you can place the payload at the same point it would enter production, preserve the retrieval and rendering steps, and verify both the model response and downstream action. A direct-prompt-only test is not a substitute.
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- Describe the target. State whether you are testing a base model, chatbot endpoint, RAG application or tool-using agent. List the components the harness will actually invoke.
- Map threats to concrete probes. Require examples for direct and indirect prompt injection, jailbreaks, harmful output, information disclosure and agency misuse as applicable. Do not accept a generic “AI security” label as evidence of coverage.
- Verify integrations and operating constraints. Confirm local-versus-hosted execution, model or API compatibility, authentication, rate limits, expected runtime and any required credentials. Check whether the scanner calls an additional model to generate or grade attacks.
- Review data flows. Identify every prompt, conversation, retrieved document and model response sent outside your environment. Remove secrets from test fixtures and document the provider, retention policy and deployment region you approve.
- Plan repeatability before the first run. Pin package versions, model identifiers, probe configuration, generation parameters and target build. Store the resulting inputs and outputs so another engineer can reproduce a finding.
- Define human review. Decide who will inspect a suspected failure, what the expected behavior is, and how false positives and false negatives will be resolved.
Build a repeatable scanning workflow
1. Prepare a representative harness
Expose the same authentication, system instructions, retrieval path and tool permissions used in the intended deployment. Use non-production secrets and synthetic records. If a tool cannot reach a component, mark that component as out of scope rather than implying it was tested.
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2. Run an exploratory scan
Start with threat families that match the application. For a RAG system, include document-carried instructions and unauthorized-document requests. For an agent, include attempts to alter tool parameters, skip approval and transfer data. For a model endpoint, include jailbreak and harmful-output probes. Keep the configuration file with the report.
3. Triage each candidate finding
- Save the exact prompt or retrieved payload.
- Save the model response, tool calls and relevant application logs.
- Record the judge’s rationale and your own expected-behavior decision.
- Repeat the case to distinguish a deterministic failure from sampling variance.
4. Convert accepted findings into checks
Encode the smallest reproducible interaction that demonstrates the policy violation. Giskard explicitly supports the pattern of generated scans followed by hand-written checks and CI replay; other frameworks require you to implement an equivalent saved test harness.
5. Run checks on every material change
Trigger the suite when prompts, retrieval code, model versions, tool schemas, guardrails or authorization logic change. Compare results only when the scanner version, provider, target build and sampling settings are recorded.
How to interpret a scan result
A failure is evidence to investigate
Inspect the attack text, the application context, the response and any side effect. A harmful-looking answer may be a false positive if the test expected the model to quote an unsafe document for analysis; an apparently harmless answer may conceal a data leak in a citation, tool call or log.
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A pass is not proof of security
Giskard warns that a clean run is not a certificate: the scan samples attacks, and a miss means only that those particular sampled attacks did not produce an accepted failure under that configuration. Sampling can miss an issue, and an automated language-model judge can be wrong in either direction. Use clean results to prioritize further assurance, not to make a security claim.
Keep scope language precise
Report exactly what was exercised: for example, “the selected garak probes against model version X through endpoint Y” or “the saved Giskard suite against the staging RAG harness.” Avoid statements such as “the AI system passed security testing” unless you can define and defend the much broader evidence behind them.
Common selection mistakes
- Choosing by brand category alone: “open-source AI scanner” does not reveal target boundary or attack depth.
- Confusing model access with application access: a model adapter cannot see retrieval authorization or tool permissions that are outside its harness.
- Ignoring provider exposure: generated attacks and judge conversations may leave your environment.
- Relying on one automated grade: preserve prompts, responses and rationale for human review.
- Failing to pin versions: probe sets, generators and documentation change; confirm package and documentation versions before relying on commands or category names.
- Stopping after discovery: a one-time scan finds possibilities; saved regression checks protect fixes over time.
Practical decision guide
- Testing a local or API-connected model with configurable probes: evaluate garak first, while documenting exactly which probes and target adapters you enabled.
- Testing a chatbot or RAG application as an interacting system: evaluate Giskard’s open-source library, especially when you want generated scenarios followed by explicit checks and CI replay.
- Designing specialized multi-step red-team scenarios: evaluate PyRIT or another orchestration framework and implement the harness, payload placement and scoring rules yourself.
- Assessing an agent’s real-world authority: prioritize an end-to-end harness that reaches tools, permissions and side effects; no model-only scan can substitute for that boundary.
The strongest program commonly combines exploratory probes with application-specific regression tests, manual review and ordinary access-control testing. Select the smallest open-source tool that reaches your real boundary, then expand coverage only when the threat model requires it.
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