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What an AI agent security testing tool needs to test
An agent is more than a model responding to text. Its risk depends on how prompts and policies interact with retrieval, memory, tools, credentials, users, and the controls around actions. A tool that tests only model responses may miss whether an agent can access another tenant’s data, exceed a user’s permissions, or carry out a consequential action without approval.
OWASP recommends structured testing before production and after material changes to prompts, tools, memory, retrieval, policies, or model providers. Its AI Agent Security Cheat Sheet also describes maintaining repeatable abuse cases and regression coverage. Check that a candidate can exercise the parts of your system that matter, rather than assuming a broad “AI security” label means full agent coverage.
Include agent-specific abuse cases
Build tests from your application’s threat model. Depending on the system, useful cases include:
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- Direct prompt override and indirect prompt injection in retrieved documents, web pages, files, or tool responses.
- Unauthorized tool calls, privilege escalation, approval bypass, and destructive actions attempted without the required approval.
- Sensitive data disclosure through responses, retrieval, memory, tools, logs, or across tenants.
- Memory poisoning, cross-session contamination, and retrieval that ignores authorization boundaries.
- Recursive tool use, excessive retries, timeouts, and token or cost exhaustion.
- MCP tool-description poisoning or shadowing, and unsafe behavior from an untrusted third-party server.
- Multi-agent delegation that crosses a trust boundary or gives a downstream agent more authority than the user.
For each case, record the expected behavior—such as deny, require approval, sanitize, isolate, time out, or alert—and compare it with what the agent actually does. OWASP’s AI/LLM Application Security Testing and Red Teaming guidance calls attention to testing agent behavior, tools, and indirect prompt injection, not just visible text output.
Treat indirect prompt injection as an authorization test
A hostile instruction in a document or tool response is not only a question of whether the model follows the instruction. Test whether the agent can use the resulting behavior to exceed the current user’s authority, access an out-of-scope tool, or expose context through an available channel. A useful test observes the attempted action and the system’s authorization and approval decision, not simply the final answer shown to the user.
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Map your agent before comparing vendors
Write down the system under test so candidates are evaluated against the same architecture. Include:
- Agent framework and version, model provider, prompts, and policies.
- Retrieval sources, memory behavior, data sensitivity, and tenant boundaries.
- Tools, API scopes, credentials, MCP servers, and agent-to-agent connections.
- Execution environment, identity model, network restrictions, and actions that need approval.
- The release and change process, including where tests could run and who reviews results.
This map helps reveal capability gaps. A prompt-focused scanner may not observe tool authorization, while a runtime monitor may not provide pre-release adversarial testing. Treat those as separate capabilities unless a vendor demonstrates otherwise for your configuration.
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Compare candidates on evidence, not labels
Ask each vendor to demonstrate the following against your architecture and test cases. The questions are evaluation criteria, not claims that any particular product meets them.
| Evaluation area | What to verify |
|---|---|
| Attack-surface coverage | Can it exercise the live agent path, retrieval, memory, tools, MCP, and relevant multi-step workflows? |
| Integration and target fit | Does it work with your framework, model provider, API or local endpoint, staging environment, identity model, and network restrictions? |
| Test quality | Can you configure and repeat cases, add your own abuse scenarios and expected denials, and understand false positives or nondeterministic results? |
| Evidence and remediation | Does each finding identify the tested agent and configuration version, scenario, observed tool action, impact, reproduction details, and practical remediation? |
| Workflow | Can tests run on pull requests, scheduled releases, and after material changes, with suitable blocking and triage controls? |
| Safe operation and data handling | What target access and credentials are needed? Where are prompts, traces, and findings stored? What retention, deletion, access, and tenant-isolation controls apply? Verify these directly; the reviewed sources do not establish vendor-specific answers. |
| Scope boundaries | Is the offering a red-team harness, AI application security test suite, runtime guardrail, inventory or risk platform, or managed assessment? Identify what it does not test rather than treating one category as proof of another. |
Ask the vendor to show the tested agent version, provider, tool policy, retrieval configuration, cases run, and observed approvals or denials. A count of attacks or a framework mapping does not by itself show that a relevant control was tested effectively.
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Run a scoped proof of concept
- Select an authorized target. Use a staging copy or another controlled environment with a representative agent configuration. Define the permitted scope, credentials, data, and actions before testing.
- Agree on a common case set. Give each candidate the same version-controlled abuse cases and expected outcomes. Include application-specific boundaries, not only generic prompt attacks.
- Observe the complete result. Ask the vendor to reproduce findings and show whether the attempted tool action was denied, approved, timed out, or otherwise handled as expected.
- Check the exported evidence. Confirm that another engineer can identify the scenario, tested configuration, observed behavior, impact, and remediation from the report.
- Exercise one release-workflow integration. Run a test through the intended CI/CD path and assess how failures are surfaced, triaged, and handled.
- Compare coverage and operating effort. Record which cases each candidate could run, what it missed, and the work required to configure, interpret, and maintain the tests.
For production agents, OWASP’s agent guidance recommends retaining evidence of the tested agent version, model provider, tool policy, retrieval configuration, abuse cases and expected results, observed approval, denial, timeout, or circuit-breaker behavior, and residual risks with compensating controls. Treat that record as part of the release evidence, not just a vendor report.
Use standards as a baseline, not a product ranking
OWASP’s Artificial Intelligence Security Verification Standard (AISVS) 1.0, released in June 2026, contains 191 requirements across 12 chapters, according to the OWASP AISVS project. It is a vendor-neutral catalogue of testable requirements that can support design, assessment, and procurement; OWASP says most production systems should aim for at least Level 2. Apply it alongside ASVS and relevant infrastructure and supply-chain controls: AISVS is intentionally focused on AI/ML-specific topics rather than replacing general security verification. Version the requirement references you use because identifiers can change.
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The NIST AI Risk Management Framework provides voluntary guidance for broader AI risk management. NIST’s current page says AI RMF 1.0 is being revised and notes that the Generative AI Profile was released on July 26, 2024. Use these frameworks to inform governance and requirements, but define and run application-specific security tests for the agent’s actual behavior.
OWASP’s GenAI testing and evaluation landscape and DevSecOps guidance mention tools and platforms as examples. Those listings can help identify candidates, but they are not independent comparative evaluations or endorsements. Verify current capabilities, compatibility, deployment options, ownership, and availability directly with each vendor.
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