The Tool Desk
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What belongs in an AI agent risk assessment?
An agent can combine model output with software functions that retrieve information, call APIs, execute code, send messages, or initiate transactions. Its risk therefore depends on the model and the surrounding system: the framework and orchestrator, connected tools and APIs, plugins or external agents, data stores and memory, credentials, human operators, and services that receive its actions.
Set the assessment boundary around every component that can affect the agent’s decisions or carry out its actions. For each one, record whether it can read, write, execute, send, delegate, or change configuration. Include supporting workloads and service identities, not only the user-facing agent.
NIST’s Center for AI Standards and Innovation (CAISI) described agent security as an active area in its January 12, 2026 request for information, including indirect prompt injection, adversarial data, insecure models, data poisoning, and harmful agent actions that need not begin with an attacker. The assessment should cover both hostile inputs and unintended behavior.
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How to assess the risk, step by step
1. Define the system and authority boundary
Draw a simple data-and-action map: what the agent receives, what it can consult, which tools it can invoke, and where its outputs or actions go. List external services and trust boundaries, such as a third-party connector, an internet-facing tool, or a separate business system. Include persistent memory, caches, and logs when they can retain or expose agent-accessible information.
- Identify the model, agent framework, orchestration layer, tools, APIs, plugins, external agents, data stores, and operators.
- For each component, state whether it can read data, modify records, execute code, send communications, initiate transactions, delegate tasks, or change settings.
- Record which components are managed internally and which depend on another organization or service.
NIST’s 2025 discussion of tool use in agent systems emphasizes that agents can manipulate tools to act, rather than merely produce text. A review that stops at model selection misses the permissions and execution environment that determine the agent’s practical authority.
2. Assess identity and credentials
For each agent instance and supporting workload, identify the principal under which actions occur and who authorized the task. An agent’s access should be attributable to that agent and task, rather than hidden behind an employee’s shared credentials.
- Does the agent have a distinct, attributable identity and an accountable owner?
- Can an audit record connect the agent’s action to the human or system that authorized its work?
- What credential type does it use, what does that credential permit, and where is it stored or transmitted?
- Is the credential scoped narrowly, short-lived where feasible, protected, rotated, and revocable?
- Can an operator revoke this agent’s access without disrupting unrelated users or services?
- Could the agent retrieve a secret from source code, configuration, a prompt, a markdown file, a log, or other data it can access?
Record the principal, credential type, scope, owner, lifetime, storage location, rotation method, revocation method, and available audit evidence. “The agent has access” is not enough to establish who can act or how that access can be withdrawn.
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In its August 27, 2026 post, NIST’s National Cybersecurity Center of Excellence (NCCoE) warns that shared human credentials weaken accountability and that static API keys and long-lived bearer tokens create exposure risks. A bearer token can be presented by whoever possesses it; a broad credential exposed in a configuration file or log may therefore enable more than the intended task.
3. Measure effective access and privilege
Build an access matrix by agent, tool, resource, and action. Assess effective rights—not just the permissions shown in an initial setup screen. Account for inherited roles, dynamically delegated scopes, cached credentials, and trust between agents.
| Agent or principal | Tool or resource | Action | Source of permission | Business need and limit |
|---|---|---|---|---|
| Record the specific agent or workload | Name the API, system, data store, or tool | Separate read, write, execute, administer, and delegate | Direct role, inherited role, delegated scope, or cached credential | State the task need, scope boundary, and any approval gate |
Use the matrix to compare each permission with the task’s minimum needs. Ask whether an agent that only summarizes records needs write access, whether a code tool can reach unrelated systems, or whether a delegated scope outlives the task. NIST’s tool-use discussion describes limiting write access and constraining broad capabilities such as code execution as ways implementations can restrict agent actions.
Then test whether those restrictions still hold when the agent receives malicious, irrelevant, or ambiguous instructions. For consequential actions, assess whether a human approval gate is needed and whether the action can be reversed.
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4. Trace data exposure
Follow sensitive information from retrieval to its final destination. Note what enters the prompt or memory, which tools and external services receive it, where outputs are sent, and what is retained in logs or other records. Identify organization and trust boundaries, along with retention and deletion paths.
- Classify data the agent can retrieve or observe, including information available indirectly through tools.
- Identify whether sensitive data enters prompts, persistent memory, caches, logs, tool requests, or generated outputs.
- Record recipients, external services, retention conditions, and deletion paths.
- Test whether untrusted content in an email, file, web page, or similar source can redirect the agent to disclose data or send it to an unauthorized destination.
NIST’s work on agent-hijacking evaluations describes malicious instructions embedded in task-relevant data as a way to redirect an agent. The OWASP Agentic Security Initiative taxonomy, surfaced in a NIST-hosted presentation as a release candidate, includes goal hijacking and tool misuse categories that can lead to data exfiltration. Treat that taxonomy as a useful evolving set of threat labels, not a settled standard.
5. Test credible threat scenarios
Use scenarios that reflect the agent’s actual tasks, tools, and data. Include attacks and failures that do not require an attacker’s direct input. For each test, record the prerequisites, task, input, attempted tool or action, control response, potential data or system impact, and recovery steps.
| Scenario to test | Question for the test | Evidence to record |
|---|---|---|
| Indirect prompt injection | Can untrusted content redirect the agent or override the task’s intended boundary? | Input source, attempted instruction, tool/action, block or alert, and resulting exposure |
| Overbroad or stolen credential | Can possession or misuse of a credential reach resources beyond the task? | Principal, credential scope, reachable resources, detection, and revocation outcome |
| Unauthorized tool call | Can the agent invoke a tool or action that the task should not allow? | Tool, requested action, permission check, approval behavior, and result |
| Harmful action without an attacker | Can normal ambiguity, error, or an unexpected result cause a damaging action? | Trigger, action, reversibility, affected systems or people, and recovery |
| Compromised or unverified tool | Can a tool return misleading instructions, expose data, or perform an unexpected operation? | Tool identity and trust, data exchanged, action attempted, and containment |
| Unintended delegation or cross-agent impersonation | Can work or authority be passed to another agent without the right identity, scope, or accountability? | Delegating and receiving principals, transferred scope, attribution, and audit trail |
NIST’s January 17, 2025 evaluation guidance says tests should adapt as defenses change, cover task-specific attacks in addition to aggregate outcomes, and consider multiple attempts. Record not only whether an attack succeeds, but what a successful action could do: outcomes can differ substantially in impact.
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6. Rate and prioritize risk in context
Describe likelihood and impact for the specific deployment instead of assigning a universal score that implies more precision than the evidence supports. Consider whether a plausible path exists, what access it requires, how often the relevant task occurs, and what a successful action could affect.
- Data sensitivity and the number or type of people whose information could be exposed.
- Privilege level, affected tools and systems, and whether authority can be delegated.
- Whether an action is reversible, and the likely financial or operational consequences.
- Whether audit evidence can identify the principal and reconstruct what happened.
- Test coverage, assumptions, and remaining uncertainty.
Prioritize scenarios with severe consequences and a plausible path, then assign a treatment owner and deadline. Record residual risk after controls are applied. This approach reflects NIST’s call to measure agent risks while accounting for the variable impact of successful attacks.
7. Verify controls continuously
For the risks identified in the assessment, verify that controls operate in the deployed system rather than relying on policy statements alone. Useful control categories include attributable identities, narrow and revocable credentials, least privilege, constrained tool interfaces, protected secrets, data minimization, monitoring and audit logs, human approval for high-impact actions, and repeatable adversarial testing.
Reassess when the model, tools, permissions, data sources, or operating context changes. A control that worked with one tool set or data source may not address a new permission path or a different kind of untrusted input. No single control eliminates agent risk.
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How to compare agent implementations
When comparing frameworks, vendors, or internal designs, use the same assessment questions for each option. The guidance cited here identifies these as relevant evaluation areas; it does not validate or rank particular products.
- Identity and attribution: Can the agent have its own identity, with actions linked to the user or system authorizing it?
- Authorization: How granular are permissions, and can delegated scopes be constrained and observed?
- Credential handling: What scope, lifetime, storage, rotation, and revocation options are available?
- Data controls: Can retrieval, isolation, retention, and egress be controlled and audited?
- Tool use: Can tools and actions be restricted, and can high-impact actions require approval?
- Audit: Do records show which principal acted, with which tool and permission, and what outcome followed?
- Evaluation: Can the team run repeatable, task-specific adversarial tests and track control behavior over time?
What current guidance does—and does not—establish
NIST’s public agent-security material describes an evolving field, not a single final agent-security standard. Keep the status of each source clear when using it to set policy or assess compliance.
- NCCoE identity and authorization concept paper: The February 5, 2026 initial public draft explores applying identity standards to agents and raises identification, authorization, auditing, non-repudiation, and prompt-injection controls. Its comment period closed April 2, 2026; it is a concept paper, not a final standard.
- NIST IR 8596: The December 2025 result is an initial preliminary draft. Its proposed focus on unique agent identities, credentials, cryptographic signing, and mutual authentication should not be presented as final guidance.
- OWASP Agentic Security Initiative taxonomy: Its appearance in a NIST-hosted presentation was as a release candidate. It offers evolving categories—including Agent Goal Hijack, Tool Misuse & Exploitation, Identity & Privilege Abuse, and Agentic Supply Chain Vulnerabilities—rather than a settled standard.
- NIST AI Risk Management Framework (AI RMF) 1.0: This voluntary framework supports integrating trustworthiness considerations into design, development, use, and evaluation. NIST’s framework page says revision is underway; it is not an agent-specific or binding security standard.
Separately, NIST’s May 18, 2026 summary of responses to its agent-security RFI says commenters widely agreed that agents present novel security threats and existing cyber practices need adaptation. That is a qualitative summary of stakeholder responses, not a measure of how often incidents occur.
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