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Evaluate the deployed agentic system—not just its language model or a jailbreak score. A production decision should be based on representative, adaptive attacks against the agent’s prompts, retrieval, memory, tools, MCP interfaces, permissions, orchestration, inter-agent messages, approval controls and downstream state changes. Require replayable evidence, map findings to business impact, verify remediation, and retest after material changes. Automation is valuable for scale and regression; experienced human testing is still needed for novel and emergent behavior.
What an agentic AI security evaluation must cover
An agent can read data, retrieve documents, call tools, write to systems, delegate work and make decisions over several turns. Its security boundary therefore includes every component that can influence an instruction or cause an external effect. Testing only the model’s response to adversarial chat prompts can miss the most consequential failures: an indirect instruction in a document, an over-privileged tool, a poisoned memory entry, a confused sub-agent or an approval step that can be bypassed.
OWASP’s guidance for advanced AI systems explicitly covers tool-calling agents, Model Context Protocol (MCP) architectures and multi-agent workflows. It warns that treating these systems as simple chatbots misses systemic vulnerabilities and workflow bypasses. Your test plan should represent the architecture that will actually run in production, including its identities, data paths and human controls.
- Model and provider: record the model name, version, hosting arrangement, safety settings and fallback models.
- Instructions: capture system prompts, developer policies, tool descriptions, routing rules and output constraints.
- Retrieval: inventory indexes, embedding models, filters, document ingestion, access controls and citations.
- Memory: distinguish session state from persistent memory, identify who can read or modify each store, and note retention and deletion behavior.
- Orchestration: document planners, routers, queues, retries, sub-agents, hand-offs and stop conditions.
- Tools and MCP servers: list schemas, credentials, scopes, network reach, write operations and trust boundaries.
- Data sinks: identify systems that receive messages, files, code, transactions or configuration changes.
- Human controls: map approval gates, escalation paths, kill switches, circuit breakers and audit logs.
For each capability, write down what the agent is allowed to read, write, execute, delegate and approve. That capability map becomes the basis for threat cases and for deciding whether a finding is merely undesirable output or an unacceptable business action.
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Scope the assessment safely before anyone attacks the system
Security testing can itself cause an outage, expose confidential data or trigger an irreversible transaction. Establish authorization and safety controls before providing a tester or platform with credentials.
Define the target and rules of engagement
- Specify the exact application, model and configuration under test, including tenant, region and environment.
- Use dedicated test accounts with the minimum permissions needed to exercise the intended workflows.
- State which production data, customer records, secrets and external services are prohibited.
- Define test hours, rate limits, escalation contacts and a stop condition for suspected impact.
- Require a mock, dry-run or isolated route for destructive actions such as sending messages, changing permissions, moving money or deleting records.
- Agree how prompts, outputs, logs, traces, tool arguments and secrets are stored, isolated, retained and deleted.
Unclear authorization, destructive testing outside an approved sandbox and vague data-handling terms are procurement red flags. The evaluator should be able to explain exactly what it will execute and how you can stop it.
Choose an assurance level
OWASP AISVS 1.0 provides risk-based verification levels and assumes that general application, infrastructure and supply-chain security are checked in parallel. Use the level as a procurement baseline, not as a substitute for tests tailored to your system.
| AISVS level | Typical use | Requirements |
|---|---|---|
| Level 1 | Baseline for every AI application | 51 |
| Level 2 | Sensitive data, consequential decisions and most production or customer-facing systems | 95 |
| Level 3 | High-assurance environments | 45 |
AISVS 1.0, released in June 2026, contains 191 requirements across 12 chapters and three appendices. Its chapters address input validation; lifecycle and change control; access and identity; supply chain; model behavior; memory and vector databases; orchestration and agent security; MCP security; adversarial robustness; and monitoring and logging. Put the AISVS version and requirement IDs in requests for proposals so two suppliers are answering the same control questions.
Build threat cases around real capabilities
A useful scenario has an entry point, an intended attacker goal, the agent capabilities involved, an expected safe behavior and a measurable business consequence. Start with the cases below, then remove or add cases according to your architecture and permissions.
Prompt injection and goal hijacking
Test direct instructions from a user as well as indirect instructions embedded in retrieved pages, uploaded files, email, code comments, tool responses and memory. Try to make the agent reveal hidden instructions, ignore a higher-priority policy, change its objective or continue an attacker’s task after the original request ends. Include multi-turn conversations in which a harmless setup gradually changes the goal.
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Unsafe or unauthorized tool use
Exercise malformed arguments, unexpected types, omitted required fields, schema confusion and tool responses designed to provoke a dangerous follow-up. Ask whether a read-only identity can reach a write operation, whether a low-trust tool can invoke another tool, and whether the agent can select an unapproved endpoint. Measure both attempted and completed calls; a blocked call and a successful state change are not equivalent outcomes.
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Attempt to obtain a more privileged identity through prompt wording, delegated tasks, cached credentials, confused deputy behavior or a sub-agent hand-off. Test whether the agent can perform an action that should require human approval by splitting it into smaller steps, changing the channel, exploiting a timeout or persuading a reviewer with a misleading summary. Verify that a kill switch and circuit breaker work while the workflow is active.
Data leakage and cross-boundary exposure
Probe leakage through responses, citations, logs, traces, retrieval results, memory, error messages and tool arguments. Test one user’s data against another user’s session, tenant and role. Include attempts to exfiltrate secrets through an allowed output channel, encode them in a file or pass them to a lower-trust agent. A refusal in the chat window is not sufficient if the secret already reached a log or downstream system.
Retrieval and memory attacks
Plant misleading or malicious documents, alter metadata, exploit stale permissions and ask the agent to trust unverified content. Check whether poisoned memory persists beyond the initiating session, whether a user can write facts for another user and whether deletion actually removes data from indexes, caches and summaries. Record the document, memory item or tool response that influenced each decision.
MCP trust-boundary failures
For each MCP server, test capability discovery, authentication, authorization, server identity and the handling of misleading tool descriptions or results. Determine whether an agent can connect to an unapproved server, whether a server can request more context than necessary and whether returned content is treated as instruction rather than untrusted data.
Role drift and multi-agent contamination
In multi-agent workflows, send one agent instructions that should remain confined to its role and observe whether another agent adopts them. Test unexpected delegation, shared memory, message tampering, identity confusion and conflicting goals. Look for contamination that appears only after several hand-offs or retries, not just in a single prompt.
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Resource exhaustion
Attempt infinite loops, retry amplification, oversized retrieved context, uncontrolled fan-out and repeated expensive tool calls. Measure denial of service and denial of wallet effects separately. A workflow that stays within a token limit may still exhaust API quotas, queue capacity, compute or a paid downstream service.
Use adaptive, representative tests rather than a static jailbreak pack
Ask an evaluator to adapt attacks to your use case, data, policies, tools and permissions. A fixed public jailbreak list can provide regression coverage, but it cannot be the sole claim of red-team comprehensiveness. Include held-out or novel cases where possible, vary the order of messages and test stateful sessions across multiple turns.
NIST’s Center for AI Standards and Innovation has emphasized that adversarial evaluations should include attacks optimized for the system being evaluated. In one 2025 experiment, the strongest new attacks optimized for an upgraded Claude 3.5 Sonnet achieved an 81% attack-success rate on held-out Workspace tasks, compared with 11% for the strongest baseline attack. This is a result from that experiment and task setup, not an enterprise average or a prediction of your system’s risk.
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- Introspection of your prompts, tool schemas, MCP servers, policies, retrieval and orchestration rather than testing a detached chatbot.
- Custom attack flows and workflow-specific assertions, alongside any stock library.
- Safe execution of destructive actions in mock, dry-run or isolated environments.
- Full multi-step traces showing messages, model decisions, tool arguments, responses, approvals and state changes.
- Deterministic or controlled replay of a sequence so a finding can be verified after a fix.
- Regression integration with CI/CD and a clear trigger for retesting changed components.
Automation improves scale, speed, repeatability and regression testing. It can also have limited adaptiveness, miss emergent behavior and create a coverage illusion. Treat the number of generated cases as a workload measure, not proof that all relevant attack paths were tested.
Demand evidence that a finding is real and actionable
For every issue, require an evidence record tied to the exact configuration. A score without a trace cannot tell an owner what to fix or whether the fix worked.
- Configuration: model and agent versions, prompts, policies, tool permissions, retrieval settings, memory state and evaluator version.
- Abuse case: the attacker’s starting access, messages or content, attack sequence and intended outcome.
- Expected safe behavior: the policy, approval requirement or isolation boundary that should have stopped the action.
- Observed behavior: responses, tool calls, arguments, returned data, approvals, denials, timeouts and circuit-breaker events.
- Provenance: which instruction, document, memory item, agent message or tool result influenced the decision.
- Impact: records exposed, state changed, privilege gained, cost incurred, availability affected and users or tenants at risk.
- Reproduction: replay steps, required seed or state, success conditions and an indication of variability.
- Severity and remediation: rationale tied to business impact, a proposed fix, compensating controls and verification results.
Complex or emergent findings should be independently reviewed. If an LLM judge labels a response safe or unsafe, ask how that judgment was validated, what disagreements occurred and whether a human confirmed the consequential cases.
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Metrics that connect attacks to deployment risk
There is no universal pass score for agentic AI security. Define each metric’s numerator, denominator, test population, severity weighting and business meaning before comparing runs. Do not compare opaque vendor scores as though they used a common benchmark.
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|---|---|---|
| Unsafe tool-call rate | Adversarial cases resulting in a prohibited or inadequately approved call divided by applicable cases | Shows whether policy enforcement survives pressure at the action boundary |
| Unauthorized state-change rate | Cases producing a forbidden write, transaction or configuration change divided by attempts | Separates dangerous effects from harmless text failures |
| Policy-bypass success | Sequences that defeat a stated control, with severity by the control bypassed | Highlights approval, identity and separation-of-duties failures |
| Data-leakage rate | Cases exposing protected data through responses, logs, retrieval, memory or tools | Captures channels a chat-only test misses |
| MCP misuse coverage | Relevant server capabilities, trust boundaries and abuse cases exercised | Shows whether MCP-specific risk was actually in scope |
| Multi-agent contamination | Cross-role or cross-tenant instructions, memory or data that influence an unauthorized agent | Measures isolation in delegated workflows |
| Coordination-breakdown severity | Impact of conflicting, missing, duplicated or reordered inter-agent messages | Connects orchestration faults to operational outcomes |
| Resource-exhaustion behavior | Cost, latency, queue, quota or availability impact under loops, retries and fan-out | Exposes denial-of-service and denial-of-wallet paths |
Report confidence and variability for stochastic tests. A lower failure rate can be meaningless if the later run used easier cases, a different permission set or a mitigation that was not actually enabled in production.
Automated tool or human red team?
The choice is usually complementary rather than either-or. OWASP’s qualitative comparison describes the following trade-offs.
| Evaluation axis | Automated tool | Human red-team service |
|---|---|---|
| Main strength | Scale, speed, repeatability and regression testing | Novel attacks, creativity, emergent behavior discovery and context-specific reasoning |
| Main limitation | Adaptiveness limits, interpretation burden and potential coverage illusion | Cost and availability |
| Tool-calling systems | Strong when well instrumented | Strong |
| MCP systems | Medium–high fit | High fit |
| Multi-agent systems | Low–medium fit | High fit |
| Evidence to demand | Custom tests, architecture introspection, safe in-environment tests, logs, replay and CI/CD integration | Clear methodology, authorization, scope, human-verified findings, threat-model fit and remediation |
Use automation for every release or policy change that should be regression-tested. Bring experienced humans into high-risk workflows, novel attack design, complex multi-agent behavior and disputes over an evaluator’s conclusion. Require both forms of testing to use the same environment definition and severity model so results can be tracked together.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Map findings to standards and governance
OWASP AISVS 1.0
Use the versioned AISVS requirement IDs to request evidence for model behavior, access control, memory, vector databases, orchestration, MCP, adversarial robustness and monitoring. AISVS complements ordinary application, infrastructure and supply-chain controls; it is not a product catalog or a complete governance framework.
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NIST AI Risk Management Framework
NIST AI RMF 1.0 is voluntary guidance for incorporating trustworthiness throughout design, development, use and evaluation. NIST’s resource page says a revised version is in progress. Use its risk-management framing to assign owners, document decisions and connect technical findings to organizational risk, while checking which regulatory and internal requirements apply to your deployment.
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NIST describes an 18-month, multistakeholder development process involving more than 240 contributing organizations. That figure describes framework development, not adoption or security performance.
Internal and jurisdictional controls
Ask suppliers to map findings to your access-control policy, privacy requirements, incident process, change-management rules and applicable jurisdiction. A technically low-severity prompt issue may become high impact when the agent can access regulated records or approve a consequential decision.
Questions to ask a security-tool or red-team provider
- Can you test our actual tool schemas, MCP servers, permissions, memory, retrieval, orchestration and inter-agent communication?
- Which tests are custom to our use case, and which are fixed benchmarks or stock-library cases?
- How do you test indirect prompt injection, privilege escalation, unsafe tool use, data leakage, role drift, approval bypass and resource exhaustion?
- Can you demonstrate a finding with complete logs, action traces, tool-call provenance and deterministic replay?
- How do you determine severity and business impact, and who verifies a finding and its remediation?
- What are your documented blind spots, automation limits and risks of LLM-as-judge scoring?
- Can destructive actions run safely in mock, dry-run or isolated environments, and can we stop a test immediately?
- How are prompts, logs, outputs, tool-access data and secrets stored, isolated, retained and deleted? Are self-hosted or zero-data-retention arrangements available?
- How does testing integrate with our release process, and what changes trigger regression testing?
- Can findings map to versioned OWASP AISVS controls, OWASP threat categories, NIST AI RMF, MITRE ATLAS and our internal governance requirements?
Ask for demonstrations against your architecture, not a slide deck about generic coverage. A provider that cannot explain its authorization model, data handling, replay process or blind spots is not giving you evidence suitable for a production decision.
Turn test results into a deployment decision
After remediation, convene the system owner, security, privacy, operations and the business owner for the affected workflow. Decide whether each residual risk is within the organization’s tolerance and record an accountable owner. A passing benchmark is not a universal threshold; the relevant question is whether the controls block unsafe actions in the deployed configuration and whether monitoring and rollback are ready.
- Block release when an attacker can make an unauthorized high-impact state change, bypass a mandatory approval or cross a tenant or identity boundary.
- Consider a constrained release when failures are low-impact, compensating controls are active, monitoring is specific and an owner accepts the residual risk.
- Require a new assessment when a mitigation changes the model, prompts, tools, memory, retrieval, policies, provider or orchestration behavior materially.
Preserve the evidence package: tested versions and configuration, threat cases, traces, approvals and denials, timeouts and circuit-breaker behavior, residual risks, compensating controls and remediation verification. This record makes later regressions distinguishable from changes in the test itself.
When to retest
OWASP’s agent guidance states: “AI agents should undergo structured security testing before production deployment and after material changes to prompts, tools, memory, retrieval, policies, or model providers.” Treat those changes as security-relevant even when the application code is unchanged.
Run a focused regression suite for every release and a broader adaptive assessment when capabilities, permissions, data boundaries or orchestration change. Retest after a new MCP server, a new write operation, a retrieval-pipeline change, a memory migration, a provider switch, a policy rewrite, a new sub-agent or a change to human approval logic. Keep a held-out set so the team does not optimize only for attacks it has already seen.
A practical pre-production checklist
- Architecture and capability inventory is complete and matches the release candidate.
- Test accounts, authorization, data-handling terms and emergency stop procedures are approved.
- Destructive actions have mock, dry-run or isolated paths.
- Threat cases cover direct and indirect injection, tools, MCP, retrieval, memory, identity, delegation, approvals, leakage and resource exhaustion.
- Tests include multi-turn and stateful sequences, adaptive attacks and held-out cases.
- Traces include prompts, model outputs, tool arguments and results, agent messages, approvals and state changes.
- Metrics have denominators, severity context and a business-risk interpretation.
- Complex findings and LLM-judge conclusions receive human verification.
- Findings map to versioned AISVS controls and relevant internal or jurisdictional requirements.
- Residual risks have named owners, compensating controls, monitoring and rollback plans.
- The exact tested configuration and evidence are retained for future replay.
- Regression triggers are defined for prompt, tool, memory, retrieval, policy, model-provider and orchestration changes.
Bottom line for procurement and engineering
The safest evaluation is a controlled exercise against the real agent architecture, with attacks that can reach the real permissions and with evidence that shows what changed in the real systems. Buy automation for repeatable coverage and release regression, add human expertise for novelty and emergent behavior, and make every result traceable to a business consequence. No single jailbreak score, benchmark pass or vendor claim can establish production readiness on its own.
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