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Testing an AI model in isolation does not establish that an AI application is secure. A meaningful assessment must trace the full control boundary: prompts and retrieved data, the model, memory, tools and APIs, application code, identities and authorizations, infrastructure, and runtime monitoring. Prompt-injection tests that stop at the model can miss whether an attacker can actually read records, invoke a privileged tool, send data externally, or alter a business system.
What the control boundary includes
Treat an AI deployment as an application and operating environment, not as a chat endpoint. Map how information and authority move through the system:
- User input, system and developer instructions
- Retrieved documents, vector stores, conversation memory and other context
- Model outputs and any downstream parser or interpreter
- Tool calls, plugins, APIs and service accounts
- Identity, authorization and tenant boundaries
- Infrastructure, secrets, data stores and network paths
- Logs, alerts, evaluation services and other monitoring components
The exact inventory varies by deployment. The important question is what an instruction or model output can trigger at each connection. OWASP’s AI/LLM testing guidance describes the model, prompts, retrieval, tools and permissions as one attack surface; testing the connections reveals failures that a model-only evaluation cannot.
Why agents make the gap more dangerous
In an agentic system, prompt injection can become an authorization failure. A malicious instruction in a web page, email or retrieved document may persuade an agent to call a tool with more authority than the requesting user has. The relevant test is therefore not only whether the model follows an untrusted instruction, but whether the resulting action can reach sensitive data or change a resource, and what the blast radius would be.
#1 Best Overall
What AI security testing should cover
AI-specific attack paths
Build tests around the components that actually exist in the deployment. Representative cases from OWASP’s application-testing guidance include:
- Direct and indirect prompt injection
- Sensitive-information disclosure and prompt leakage
- Unsafe output handling when generated text is passed to code, queries, markup or workflows
- Excessive agency, including tool actions beyond the user’s entitlement
- Retrieval authorization failures and poisoned documents or indexes
- Unbounded consumption that can exhaust model, tool or infrastructure resources
For every finding, demonstrate the path from untrusted input to an observable effect. A harmless-looking answer is not evidence of safety if the same response is later interpreted as a command by application code.
Conventional security controls
AI-specific checks do not replace ordinary application, infrastructure or supply-chain assurance. OWASP’s Artificial Intelligence Security Verification Standard (AISVS) states that its AI requirements are intentionally narrow and that general security topics should be assessed in parallel. Review authentication, authorization, session handling, secrets, dependency and build integrity, network isolation, data protection, logging, incident response and denial-of-service controls using the standards appropriate to the environment.
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- Define the system and intended use. Document models, prompts, data sources, users, third-party services, deployment locations and every action the system is allowed to take. Record which uses are prohibited as well as those that are supported.
- Map data and authority flows. For each data store and tool, identify the user, service or agent identity presented at the boundary. Enforce authorization in application and service controls; a system prompt is not an access-control mechanism. Include tenant separation, vector-store permissions and outbound destinations.
- Create a threat-informed test plan. Select prompt-injection, disclosure, unsafe-output, retrieval, tool-abuse and resource-exhaustion tests that match the mapped components. Include abuse cases in which a compromised document or tool response supplies the malicious instruction.
- Pair AI tests with baseline testing. Apply AISVS for AI-specific requirements while running relevant application, infrastructure and supply-chain assessments. Track dependencies and shared services that sit outside the model team’s ownership.
- Use deployment-like conditions. Test the versions, retrieval indexes, policies, identities, network routes and monitoring used in production or a faithful staging environment. Record test sets, prompts, tools, expected outcomes, metrics and known blind spots.
- Reassess during operation. Repeat evaluation after material changes to the model, retrieval corpus, tools, permissions, data or orchestration code. NIST’s AI Risk Management Framework (AI RMF) calls for testing before deployment and regularly while systems operate, with monitoring and documented measurement methods.
- Assign and retest findings. Report the affected boundary, conditions, demonstrated impact, owner, mitigation, residual risk and retest evidence. Independent review can expose assumptions missed by the implementation team.
Frameworks: complementary roles, not security certificates
| Resource | Best use | Scope or limitation |
|---|---|---|
| OWASP AISVS 1.0 | Testable AI-security requirements for design, development, assessment and procurement. | OWASP describes it as intentionally narrow; general application, infrastructure and supply-chain security remain parallel work. Version 1.0, announced in June 2026, has 191 requirements across 12 chapters and three appendices, with verification levels 1, 2 and 3. |
| NIST AI RMF | Governance and risk management across Govern, Map, Measure and Manage, including evaluation, documentation and operational monitoring. | It is a risk-management framework, not a standalone attack checklist or certification. |
| OWASP AI/LLM testing guidance | Practical test cases for language-model applications, retrieval, agents, tools and authorization. | Check the current OWASP project edition; the referenced guidance points to the 2025 Top 10 for LLM Applications. |
| OWASP Autonomous Penetration Testing Standard (APTS) | Governance for autonomous penetration-testing platforms, including scope, safe autonomy, oversight and accountability. | OWASP presents it as a governance standard, not a testing methodology. Its project page lists version 0.1.0 and 173 tier-required requirements across eight domains and three compliance tiers: 72 cumulative for Tier 1, 157 for Tier 2 and 173 for Tier 3. These are project-page counts, not independent certification or proof of adoption. |
Compare these resources by scope, lifecycle coverage, requirement testability, deployment context and how they complement baseline assurance. Completing a checklist or naming a framework does not prove that a live system is secure.
Red teaming is not always penetration testing
NIST’s adversarial-machine-learning taxonomy distinguishes traditional cybersecurity red teaming, which emulates an adversary against an organization’s broader posture, from penetration testing aimed at a defined application or system. AI discussions often use “red teaming” for model exercises performed by evaluators outside normal operating conditions. Put the engagement scope in writing: assets, identities, allowed tools, data, production access, safety limits, success criteria and reporting obligations. Otherwise, two teams can use the same label for materially different evidence.
What an assessment can and cannot prove
NIST identifies confidentiality, integrity and availability as relevant to AI systems, their training and output data, and the hardware and software beneath them. It also cautions that existing guidance does not comprehensively address every AI attack type or the full complexity of AI attack surfaces, including areas such as evasion, model extraction, membership inference and availability. Findings should therefore state the tested conditions and residual risk rather than claiming universal safety.
NIST describes AI security and resilience as an active research area whose threats and potential solutions are changing rapidly. Keep test methods, assumptions and metrics versioned, and revisit them when the system or threat landscape changes.
Practical release gate
Before approving an AI feature, require evidence that:
Best Value
- Every model, data source, tool, identity and downstream interpreter is inventoried.
- Authorization is enforced by services and data stores, including tenant and retrieval boundaries.
- Prompt injection and tool-abuse scenarios have demonstrated impact tests, not just response-quality scores.
- Conventional application, infrastructure and supply-chain controls have been assessed alongside AI-specific requirements.
- Production-like monitoring can detect abnormal tool calls, data access, output handling and resource use.
- Owners, remediation deadlines, residual risks and retest results are documented.
This approach tests whether the deployed system can be made to do something harmful, who can make it do so, and which controls limit the consequences—questions a model-only benchmark cannot answer.
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