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What an AI-agent security audit should cover
An agent may retrieve files, read email, call business applications, run code, or take actions based on model-generated plans. That creates familiar software risks alongside risks from model behavior: for example, untrusted content could influence a tool call, or an agent could pursue a harmful outcome without an attacker deliberately prompting it.
NIST’s January 12, 2026 CAISI request for information (RFI) discusses agent systems that can plan and take autonomous actions affecting real-world systems. NIST’s February 5, 2026 NCCoE concept paper focuses on agent identity and authority. Treat these as useful descriptions of emerging risks and work—not as a finalized universal audit standard.
The audit should establish what agents exist, trace their data and authority, test realistic failure scenarios, verify controls around consequential actions, and record evidence and remediation. Include deployed systems, pilots, and agent-like features embedded in software or services; an organization can have agents without having launched a project formally labeled “AI agent.”
#1 Best Overall
1. Discover agents and define the audit scope
Build an inventory that captures capability, not just product names
Ask business units, IT, security, procurement, and AI governance owners about tools in production, pilots, and embedded workflows. For each deployment, record its owner, purpose, model and provider where known, operating environment, connected data, tools, downstream systems, and degree of autonomy. Identify whether it can read, write, execute code, communicate outside the organization, change access, or trigger production, administrative, or financial actions.
Record how the agent is invoked and by whom, what human review occurs, and which identity or delegated credentials it uses. If a vendor or internal team cannot explain a connection or permission, mark that uncertainty as an audit finding to resolve rather than assuming the access is harmless.
Set boundaries for the review
Define which business processes and environments are in scope, including development, test, and production where relevant. Identify systems that must not be touched during testing, who can approve controlled tests, and how to stop an agent if it behaves unexpectedly. Prioritize deployments with sensitive data, broad tool access, independent action, or consequences that are difficult to reverse.
2. Trace data flows and trust boundaries
Map the route from user instructions and connected content to the model, tools, downstream systems, and recipients. Include retrieved documents, incoming email, web pages, tickets, and tool responses: each can contain untrusted text that may influence an agent’s behavior. Determine whether the system treats such content as data rather than as authoritative instructions.
Rank #2
For each path, identify sensitive information the agent can retrieve or generate, where that information can go, and what prevents disclosure through a response or tool call. Inspect retrieval permissions, output filtering, recipient controls, and any restrictions on passing data between tools. A data-flow diagram should show trust boundaries and the controls applied at each transition, not merely the model’s input and output.
3. Test threats and failure scenarios
Use controlled, authorized tests that reflect the agent’s real connections and permissions. Preserve the scenario, expected behavior, actual behavior, evidence, potential impact, and whether another run reproduced the result. Avoid testing against live customers, production data, or external recipients unless the test has explicit approval and safeguards.
| Risk area | Audit question | Evidence to request |
|---|---|---|
| Indirect prompt injection | Can hostile content in a document, message, web page, or tool response redirect the agent, misuse a tool, or disclose information? | Controlled test cases, retrieved-content handling, tool-call records, red-team results, and relevant incident records. NIST CAISI RFI, January 12, 2026. |
| Excessive agency | Does the agent have more tools, permissions, or independent action than its task requires? | Tool inventory, permission scopes, configuration, identity-provider grants, and execution policies. OWASP Gen AI Security Project, “LLM06:2025 Excessive Agency.” |
| Identity and delegated authority | Can each agent and action be attributed to an identity and an approved authority chain? | Agent identity design, authorization decisions, delegated credentials, and audit records. NIST NCCoE concept paper, February 5, 2026. |
| Unintended or misaligned action | Could the system pursue a proxy objective or take a harmful action even without adversarial input? | Objective and policy definitions, scenario tests, exception handling, and approval evidence. NIST CAISI RFI, January 12, 2026. |
| High-impact execution | Are destructive, financial, administrative, or external actions previewed, approved, independently checked, and recoverable? | Approval records, policy-service logs, interruption and rollback exercises, and replay protections. OWASP AI Agent Security Cheat Sheet. |
| Data exposure and output handling | Can sensitive data leak through outputs or downstream tools, and are outputs validated before they trigger execution? | Data-flow diagrams, output schemas, filtering rules, rate limits, and scope limits. OWASP AI Agent Security Cheat Sheet. |
| Monitoring and response | Can teams identify undesirable actions and contain them before their impact grows? | Alerts, rate limits, runbooks, exercise results, and action and decision trails. OWASP AI Agent Security Cheat Sheet and “LLM06:2025 Excessive Agency.” |
Exercise the paths that matter most
For each high-priority deployment, test whether connected content can steer the agent toward an unauthorized action; whether it retrieves or exposes more data than the task needs; and whether a tool can be called with an unexpected target, argument, or recipient. Test relevant model or component supply-chain concerns, as well as failure cases in which an objective or proxy measure leads to a harmful result without an attacker.
OWASP’s excessive-agency example describes a malicious email steering a mailbox assistant toward scanning an inbox and forwarding sensitive information. Adapt the scenario to your own workflow: test whether the agent can be induced to exceed its purpose, then verify that permissions and execution controls—not just a prompt telling the model to behave—block the result.
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4. Verify agent identity and least-privilege access
Determine whether each agent has an attributable identity and whether tool connections are authorized for the specific task. Compare the system’s actual scopes and functions with what the task requires. A tool that summarizes email, for example, may need read access without permission to send messages.
Review delegated credentials, token handling, authorization decisions, and logs that connect an action to the responsible agent, user, and approval where applicable. Ask whether access is limited by data, operation, and duration where the system supports it, and whether teams can revoke or change it promptly. NIST’s NCCoE concept paper treats identification, authorization, and auditing as important problems because agents can reach diverse datasets, tools, and applications.
OWASP’s “LLM06:2025 Excessive Agency” recommends reducing excess functionality and permissions, including read-only OAuth scopes when those are sufficient. A policy statement or a restrictive system prompt is not a substitute for removing unnecessary capability at the tool and authorization layers.
5. Match autonomy and approvals to action impact
Classify actions by their potential impact and reversibility. Reading or drafting may need a different control level from sending an external message, changing access, deleting records, moving money, or altering production systems. The classification should follow what the agent can actually do, not what its interface calls the feature.
Rank #4
For high-impact or irreversible actions, verify that the agent presents a clear action preview and requires explicit approval before execution. Check that the approval is bound to the actual proposed action—such as its target, scope, and material parameters—so an approved proposal cannot silently become a different operation. Where risk warrants it, use an independent execution check to validate scope, privilege, and approval rather than relying on the agent that proposed the action.
Test whether operators can interrupt work and whether recovery or rollback is possible. For operations that cannot be rolled back, define the compensating response in advance. OWASP’s AI Agent Security Cheat Sheet recommends explicit approval for high-impact or irreversible actions and separating proposals from independent execution validation for consequential operations.
6. Inspect safeguards between model output and execution
Trace how generated outputs become tool calls or user-visible results. Confirm that outputs are checked against expected schemas and policy before execution, and that sensitive data is filtered where appropriate. Examine limits on tool scope and call rate, along with protections against duplicate or replayed high-impact operations.
Test failure behavior as well as successful controls: if a policy service, validation step, or audit component is unavailable, does risky execution stop safely, or can the agent proceed without the check? Confirm that approval cannot be bypassed through retries, alternate tools, or a change in the proposed action after review.
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Inspect whether the organization records enough about decisions and actions to investigate what happened: the initiating request, relevant context, identity, authorization, tool call, approval, and outcome where available. Logs should support attribution and incident review while being protected against unauthorized access or alteration; avoid collecting sensitive content indiscriminately when a more limited record will serve the purpose.
Exercise alerting and response. Can an operator recognize suspicious tool use, stop an agent or revoke its access, preserve evidence, and determine what downstream systems or recipients were affected? Test restoration or rollback where possible. Rate limits can reduce damage while an issue is being detected; logging and monitoring help identify undesirable downstream actions, as OWASP guidance notes.
8. Report findings and prioritize remediation
For each finding, document the affected agent and process, tested scenario, control evidence, observed gap, plausible business impact, accountable owner, remediation, and residual risk. Distinguish a demonstrated behavior from a theoretical concern, and record test limitations so decision-makers can judge confidence without mistaking an untested case for a safe one.
Use a consistent risk method already accepted by your organization where possible. Consider data sensitivity and exposure, number and privilege of tools, autonomy and action impact, identity and delegated authorization, monitoring and auditability, and test coverage for both adversarial and non-adversarial failures. These are practical comparison dimensions, not an official scoring scale. When comparing deployments, use the same dimensions so teams can see why one warrants tighter controls or faster remediation.
Map relevant findings into existing security and AI risk registers, link each treatment to an owner and target decision, and document accepted residual risk. OWASP’s AIVSS Agentic AI Core Security Risks v0.5 describes structured scoring as useful for audits, risk registers, and treatment decisions, with mappings to NIST CSF, NIST AI RMF, ISO/IEC 27001/27002, and ISO/IEC 23894. A mapping can help locate existing controls; it does not prove that every agent-specific failure mode is covered.
How to use current frameworks without overstating them
NIST AI RMF 1.0 is a voluntary framework released January 26, 2023, intended to help integrate trustworthiness into AI design, development, use, and evaluation. NIST’s current AI RMF page says the framework is being revised. Use it as a risk-management backbone, record the version applied, and do not describe it as an agent-specific certification.
NIST’s AI Agent Standards Initiative describes work on voluntary guidance, interoperability, agent authentication and identity infrastructure, and security evaluations; its page was updated August 14, 2026. The January 2026 CAISI RFI and February 2026 NCCoE concept paper describe questions and project work, not settled universal audit requirements. Check current framework and initiative materials when planning or updating an audit, and distinguish evolving guidance from an adopted organizational control.
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