AI agents are changing a familiar application-security problem into a much more complex one.

Traditional software generally receives a request from a user, verifies identity, checks permissions and performs a predefined operation. An AI agent can sit between those stages. It may interpret a high-level objective, retrieve data, choose tools, make several intermediate decisions and perform multiple actions before the workflow finishes.

That autonomy creates a new identity question:

When an AI agent performs an action, whose authority is it actually using?

The user’s?

The agent’s?

The application’s?

A service account’s?

Or some combination of all four?

This is becoming important enough that NIST’s National Cybersecurity Center of Excellence launched dedicated work in 2026 around Software and AI Agent Identity and Authorization, specifically examining how established identity standards and access-control practices should apply to autonomous agents. NIST highlights identification, authorization, auditing and non-repudiation as core areas that need stronger treatment as agents gain access to more data, tools and applications.

For AI product teams, the security objective should remain simple even when the architecture becomes complex:

An AI agent should never gain authority merely because the model believes an action is appropriate.

Authentication establishes identity.

Authorization establishes permission.

The LLM may help interpret intent, but critical permission decisions should remain deterministic, auditable and outside the model. OWASP explicitly recommends that privilege separation and authorization bounds checks be enforced independently from the LLM.

Why Identity Matters More When AI Can Act

A chatbot that only answers questions has limited direct authority.

An agent connected to tools can operate on behalf of users.

It might read documents, create support tickets, modify records, interact with source repositories or initiate workflows in other business systems. NIST’s current agent-identity work is motivated precisely by this transition from generative output toward systems capable of autonomous action with limited human supervision.

That means every meaningful agent action should have an answer to three questions:

Who initiated the workflow?

Which agent performed the operation?

Under whose permissions did the downstream system authorize it?

If those questions become ambiguous, security problems follow quickly.

A backend log that says only ai-service performed update may be technically accurate but operationally weak. It does not necessarily tell investigators which user caused the action or whether that user should have possessed the underlying permission.

As AI systems become more autonomous, identity needs to survive the complete workflow.

Authentication and Authorization Are Different Problems

Authentication answers:

Who is this actor?

Authorization answers:

What is this actor allowed to do?

This distinction becomes particularly important in AI applications because several identities may participate in one interaction.

A user authenticates to the SaaS application.

The application starts an AI agent.

The agent selects a tool.

The tool calls an internal API.

The API reaches a database.

At each stage, the system needs to understand which authority applies.

The fact that the user successfully logged in does not mean every action selected by the agent should be permitted.

Likewise, the fact that the AI service has valid backend credentials does not mean every user interacting with the AI should inherit those credentials’ full capabilities.

NIST’s current project is explicitly investigating standards-based approaches for identifying agents and managing which actions they are authorized to take.

AI Creates a Permission Chain

It is useful to think about agent security as a permission chain rather than one login event.

The chain begins with a human or another system initiating an operation.

That request reaches an AI application.

The application gives an agent a set of tools.

Each tool interacts with another system under some identity.

That downstream system finally determines what can happen.

A secure design keeps the authority narrow throughout this chain.

A dangerous design introduces a privilege jump somewhere in the middle.

For example, an ordinary user may have read access to only their own documents.

The AI agent then calls a document service using a shared administrator credential with access to every user’s files.

The application has effectively transformed a limited user into a potential route toward global data access.

OWASP identifies this exact class of problem under Excessive Agency, warning that extensions designed to operate in a user’s context can become dangerously overprivileged when they connect to downstream systems using generic high-privilege identities.

User Identity Should Survive the AI Layer

One of the strongest architectural principles for AI agents is preserving the original user’s security context.

Suppose an employee asks an enterprise assistant:

“Summarize my current projects.”

The agent calls a document retrieval tool.

The tool should not simply search every project accessible to the AI service.

It should search the subset available to that employee.

Now suppose the user asks:

“Update the project status.”

The same principle applies.

The backend should determine whether that specific user has update permission.

The AI should not become a new authorization universe with broader access than the application itself.

OWASP recommends tracking user authorization and security scope so agent extensions perform actions on downstream systems in the context of the relevant user and with the minimum necessary privileges.

This is one of the most important differences between a secure AI integration and an overprivileged one.

Agent Identity Is Not the Same as User Identity

At the same time, an agent may need its own identity.

That does not mean the agent should replace the user.

The two identities serve different purposes.

The user’s identity tells the application on whose behalf work is being performed.

The agent’s identity tells the infrastructure which automated actor executed the operation.

Both can matter during authorization and auditing.

Imagine a production system where humans, scheduled jobs and several AI agents can all modify records.

A useful audit log should ideally distinguish among them.

An operation might therefore carry both:

the initiating user,

and the acting agent.

This dual-context approach makes investigations easier and enables policies that distinguish human actions from autonomous ones.

NIST’s 2026 agent identity work specifically includes agent identification, authorization, auditing and non-repudiation within the problem space it is examining.

Application Identity Creates Another Layer

Many AI systems also use application or service identities.

These are machine credentials used to access infrastructure.

Service identities are not inherently unsafe.

The risk appears when a broad service identity silently becomes the effective authorization layer for every AI user.

Imagine an AI assistant that uses one service credential capable of reading every CRM account.

Even if the frontend correctly authenticates individual users, the CRM itself now sees only the shared AI service.

Unless the system preserves and re-enforces user-level authorization elsewhere, tenant or role boundaries may disappear.

This is a classic confused-identity problem applied to AI.

The application may know who the user is.

The backend that matters may not.

Model Capability and Authorization Must Remain Separate

An AI model may understand what an administrator does.

That does not mean it should be allowed to perform administrator actions.

A model may correctly infer that deleting an account would resolve a particular support problem.

That does not establish authorization to delete it.

This separation between reasoning capability and application authority is fundamental.

OWASP states that critical controls such as privilege separation and authorization bounds checking should not be delegated to an LLM because these controls need deterministic and auditable enforcement.

The LLM can produce a recommendation.

The backend decides whether the operation is permitted.

That distinction remains valid even as models become significantly more capable.

The LLM Should Never Be the Final Authorization Layer

One dangerous design pattern is placing permission rules inside the system prompt.

A developer might tell the agent:

“Only administrators may delete accounts.”

The agent generally follows the instruction.

Testing appears successful.

This may look like access control.

It is not strong access control.

Prompt injection, context manipulation, model errors or future behavioral changes can influence the model’s decision process. OWASP therefore recommends keeping strict security controls outside the LLM and specifically warns against relying on system prompts for authorization or privilege separation.

A secure backend should instead receive the deletion request and verify:

the authenticated user,

their role,

the target account,

the applicable business rules,

and any required approval.

If those checks fail, deletion does not happen regardless of what the model requested.

Prompt Instructions Can Guide Behavior, but Not Define Permission

This does not mean system prompts are useless.

They can guide normal behavior.

They can tell an agent which operations are expected.

They can reduce unnecessary tool use.

They can help the model understand workflows.

But they should not be responsible for preserving confidentiality or enforcing access control.

A useful mental model is:

Prompts describe policy to the model. Code enforces policy on the system.

The first improves behavior.

The second creates a security boundary.

Least Privilege Should Start at the Agent Level

Least privilege is particularly powerful for AI agents because it reduces the consequences of model failure.

An agent that can only read public documentation presents one risk profile.

An agent that can read, modify and delete production data presents another.

OWASP’s Excessive Agency guidance recommends minimizing both the number of extensions available to agents and the permissions those extensions hold on downstream systems. It specifically advises limiting agent capabilities to the minimum necessary for the intended operation.

This means product teams should resist the temptation to connect every available tool to one universal assistant.

Convenience creates authority.

Authority creates attack surface.

Agent Permissions Should Reflect the Current Task

An even stronger architecture can make permissions task-specific.

An AI system may support several workflows.

Document research.

Customer support.

Account administration.

Deployment operations.

Those tasks do not require the same authority.

Instead of giving one agent every capability permanently, the application can provide a limited capability set appropriate to the current workflow.

A research operation receives read access.

A drafting operation may receive permission to create temporary content.

Publishing may require a separate permission.

Administrative changes may require additional authorization.

This reduces the amount of authority available if the agent is manipulated.

Read and Write Permissions Should Be Separated

Read access and write access should rarely be treated as equivalent.

Many AI workflows require information retrieval much more frequently than state changes.

A support assistant may need to read customer data to answer questions.

That does not automatically justify permission to modify customer accounts.

A coding assistant may need to inspect a repository.

That does not automatically justify production deployment authority.

OWASP uses similar examples when discussing Excessive Agency, including tools that need only read access but receive modification or deletion capabilities as well.

Separating read from write capabilities creates a useful containment boundary.

Destructive Actions Deserve Stronger Authorization

Deleting data, changing permissions, transferring ownership or performing irreversible operations should not receive the same treatment as ordinary retrieval.

Even if an AI agent legitimately needs access to such functionality, stronger controls may be appropriate.

That can include explicit authorization checks, recent user reauthentication, contextual confirmation or human approval.

OWASP recommends requiring human approval for high-impact operations and enforcing authorization in downstream systems rather than relying on model judgment alone.

The objective is not to add friction everywhere.

It is to place stronger controls where a wrong decision has greater consequences.

Human Approval Should Not Replace Authorization

Human approval is useful.

It is not a substitute for proper access control.

Suppose an ordinary user convinces an AI agent to request an administrator-only operation.

The application presents a confirmation button:

“Do you want to continue?”

The user clicks yes.

If that is the only protection, the system is still broken.

A user cannot approve themselves into privileges they do not possess.

Authorization should run first.

Human confirmation answers:

“Does the authorized user intend to perform this action?”

Authorization answers:

“Is this user allowed to perform it at all?”

Both can be necessary.

They solve different problems.

AI Agent Authorization Should Be Object-Aware

Permission checks need to consider not only which function is requested but also which object it affects.

A user may be allowed to call updateProject.

That does not mean they can update every project.

A customer may be allowed to retrieve an invoice.

That does not mean they can retrieve another customer’s invoice.

An AI model may generate a perfectly valid object identifier while still referring to an unauthorized resource.

This is where AI agent security converges with traditional API security.

Function-level authorization is not enough.

Object-level authorization matters.

The same permission checks that protect a direct API request should also protect an API request originating from an AI agent.

Tenant Boundaries Must Survive AI Workflows

Multi-tenant SaaS products are especially sensitive to authorization mistakes.

An AI assistant may serve hundreds or thousands of customer organizations through shared infrastructure.

If the agent accesses backend systems through a global service account, tenant isolation can become dependent on application logic inside the AI layer.

That is fragile.

Tenant identity should remain part of the downstream authorization process.

If Tenant A asks the agent to search records, only Tenant A’s authorized records should become eligible.

If Tenant A asks for an update, the backend should independently verify that the target object belongs within Tenant A’s scope.

The model should not be expected to remember tenant boundaries correctly under every possible prompt or context.

RAG Authorization Should Happen Before Retrieval

The same principle applies to Retrieval-Augmented Generation.

A RAG system often contains documents from different users, departments or tenants.

Semantic similarity determines relevance.

Authorization must determine eligibility.

The system should not retrieve every semantically relevant document and then ask the LLM not to disclose unauthorized results.

A safer architecture first defines the authorized document set and then performs retrieval inside it.

This preserves deterministic authorization before sensitive information reaches model context.

It also reduces the chance that a model-level weakness becomes a data-access vulnerability.

AI Agent Memory Needs Authorization Too

Agent memory is another form of data.

If an AI system stores long-term information about users, projects or previous tasks, that memory needs ownership and access boundaries.

An agent should not retrieve another user’s memory simply because it is semantically relevant.

Shared organizational memory may be legitimate.

Private memory may not be.

Security teams should therefore treat persistent AI memory similarly to other application data stores.

Who created it?

Who owns it?

Who can read it?

Who can modify it?

How long does it remain available?

Authentication and authorization do not stop because the storage format happens to be “AI memory.”

Tools Should Enforce Their Own Security Boundaries

AI architectures often expose tools to agents.

Those tools may interact with databases, APIs or external systems.

A secure tool should not assume that any request coming from the AI agent is authorized.

OWASP’s Excessive Agency guidance explicitly recommends complete mediation, meaning downstream systems should validate requests against security policies instead of relying on the LLM to decide whether the action is allowed.

This creates defense in depth.

The model may choose the wrong tool.

The backend still checks authorization.

The model may produce the wrong target ID.

Object-level authorization still applies.

Prompt injection may manipulate agent intent.

The permission boundary still survives.

Tool Credentials Should Be Narrowly Scoped

An agent should not receive broad credentials merely because broad credentials simplify development.

If a tool only needs to read one dataset, its credential should not also permit writes across unrelated resources.

If it operates for one business function, it should not automatically gain access to every service in the organization.

OWASP recommends limiting extension permissions on downstream systems and applying appropriate database or service permissions to the identity used by the extension.

This principle matters because the model does not necessarily need to steal a credential to abuse it.

If the agent already possesses broad authority, manipulated reasoning may be enough to make use of that authority.

Short-Lived Authority Can Reduce Risk

Long-lived broad credentials increase the duration and potential impact of compromise.

AI agent architectures can benefit from more contextual authorization.

A particular workflow may receive permission for a limited task or period instead of permanent broad access.

This idea aligns with the broader direction of current agent-identity work, where NIST is investigating standards and best practices for securely managing agent authority rather than treating agents as permanently trusted software identities.

The implementation will vary by environment.

The principle is straightforward:

Give the agent the authority required now, not every authority it might need later.

Delegated Authorization Needs Clear Semantics

AI agents frequently act on behalf of a human.

This creates delegated authorization.

The system needs to distinguish:

“This user can perform the operation,”

from:

“This agent itself has independent authority to perform the operation.”

Those are not always equivalent.

A personal assistant may be permitted to schedule meetings only for the authenticated user.

An internal automation agent may have organizational authority independent of one specific employee.

A deployment agent might be permitted to act only after an approved release workflow.

Security architecture should make these relationships explicit.

Otherwise, a generic “agent token” can silently blur several very different trust models.

Agents Acting on Behalf of Users Should Not Gain Extra Privilege

The safest default is that delegation should not increase authority.

If the human user cannot access a resource directly, the AI agent acting for that user should generally not gain access merely because it communicates through a privileged backend.

OWASP recommends executing agent extensions in the user’s security context and with minimum necessary privileges for precisely this reason.

There may be legitimate workflows where an agent possesses additional organizational authority.

Those should be explicit exceptions with strong controls, not accidental side effects of service-account design.

Agents With Independent Authority Need Their Own Governance

Some agents are not simply assistants for individual users.

They may perform background operations for the organization.

An AI agent might review infrastructure events, update internal tickets or perform routine automated maintenance.

In this model, the agent needs its own authority.

That authority should be governed similarly to other privileged machine identities.

Which systems may it access?

Which operations may it perform?

Who can change its permissions?

Which logs record its actions?

How can its access be suspended?

What happens if the agent behaves unexpectedly?

NIST’s agent-identity initiative reflects the growing importance of treating autonomous agents as actors that may need distinct identification, authorization and auditability.

Multi-Agent Systems Complicate Authorization Further

A single-agent architecture is already complex.

Multi-agent systems create more trust relationships.

One agent may gather information.

Another may analyze it.

A third may execute actions.

The danger appears when permissions transfer implicitly between them.

A research agent with access only to public information should not automatically inherit the authority of an execution agent.

Likewise, an agent receiving a message from another agent should not assume that message itself proves authorization.

OWASP’s 2026 agentic-security framework treats agents that plan, act and make decisions across complex workflows as a dedicated security surface, reflecting the growing need to reason about these interconnected systems.

Identity and authority should therefore remain explicit even between agents.

An Agent Message Is Not an Authorization Token

This sounds obvious in traditional distributed systems.

It can become less obvious in AI systems.

Agent A may tell Agent B:

“The user approved this operation.”

Agent B should not automatically trust that sentence as proof.

Authorization needs an authenticated, verifiable mechanism.

Natural-language communication is useful for reasoning.

It should not replace security credentials.

This distinction becomes particularly important in systems where agents can be manipulated through untrusted context.

The security system should trust cryptographic or deterministic authorization state, not a model-generated narrative about permission.

Prompt Injection Should Not Become Privilege Escalation

Prompt injection is one of the strongest arguments for externalizing authorization.

Suppose an attacker manipulates an agent into requesting access to a privileged resource.

If the backend independently evaluates the authenticated user’s permissions, the request should fail.

The model was manipulated.

The security boundary survived.

OWASP recommends least privilege, external permission controls and human approval for high-risk actions as mitigations around prompt injection and excessive agency.

The security objective is therefore not necessarily to guarantee that the agent can never be manipulated.

It is to guarantee that manipulation does not automatically create new authority.

Prompt Injection and Authorization Are Different Layers

This distinction is worth emphasizing.

Prompt-injection defenses attempt to reduce unwanted model behavior.

Authorization limits what happens after unwanted behavior occurs.

An application can therefore have imperfect prompt-injection resistance while still preserving important security boundaries.

An injected instruction causes the agent to request another user’s confidential record.

The retrieval API rejects it.

The attack fails at authorization.

This is much stronger than relying only on the model to decide that the request is suspicious.

Excessive Permissions Amplify Model Errors

The impact of AI failure depends heavily on available authority.

Two agents may use the same model.

One has read-only access to public documentation.

The other has administrator access to production systems.

A similar reasoning error produces very different consequences.

OWASP identifies excessive permissions as one of the core root causes of Excessive Agency and specifically warns about agents using downstream identities with unnecessary read, write, insert or delete permissions.

This makes permission architecture one of the most effective ways to control AI risk.

You do not need a perfectly reliable model to build a bounded system.

Authorization Should Be Checked at Every Sensitive Transition

One common mistake is performing authorization once at the beginning of a long agent workflow.

The user starts an authorized task.

The agent then performs ten actions.

The application assumes all ten inherit the legitimacy of the original request.

That can become unsafe as workflows branch.

The agent may discover new information.

It may select an unexpected tool.

The target object may change.

A later action may carry much greater impact than the initial one.

Sensitive transitions should therefore receive authorization appropriate to the action being performed.

This is the principle of complete mediation that OWASP recommends for downstream LLM extension requests.

Action-Level Authorization Is More Important as Agents Become Autonomous

The more autonomy an agent receives, the less reasonable it becomes to assume that one initial user approval covers everything that follows.

A user may ask:

“Help me organize my customer accounts.”

The agent could interpret that objective in many ways.

Reading account metadata is one action.

Changing ownership is another.

Deleting duplicate accounts is another.

Sending messages to customers is another.

A high-level goal should not automatically authorize every technically related operation.

Authorization should remain connected to specific capability boundaries.

Approval Should Escalate With Impact

A useful design principle is graduated authority.

Low-impact, reversible actions can often occur with relatively little friction.

Higher-impact operations receive stronger checks.

A read-only lookup may run automatically.

Editing a draft may require ordinary user authorization.

Publishing externally may require confirmation.

Changing account permissions may require stronger verification.

OWASP recommends human approval for high-impact actions specifically as a way to reduce the consequences of excessive autonomy.

This allows AI products to remain useful without flattening every operation into the same trust level.

Approval Fatigue Can Weaken Security

Requiring confirmation for every trivial tool call can make an agent frustrating.

It can also weaken oversight.

Users who approve dozens of harmless requests may stop reading them carefully.

High-value security decisions become visually indistinguishable from routine actions.

A stronger design concentrates human attention on operations where user intent matters most.

Security controls should create meaningful friction rather than constant friction.

Permission Boundaries Should Be Understandable to Users

Agent interfaces should make important authority visible.

Users should understand when an agent is only reading information versus modifying state.

They should be able to recognize when an external system will be contacted.

They should know when an operation affects another person, a shared workspace or production environment.

This supports informed approval.

The underlying authorization still belongs in deterministic application controls.

The interface helps the user understand what those controls are permitting.

Authentication for High-Risk Actions May Need to Be Fresh

A long-running AI session can create another challenge.

A user may authenticate once and leave the session active for a long time.

Later, the agent attempts a highly sensitive action.

Depending on the risk model, relying on the original authentication event may not be sufficient.

Some operations can justify stronger or more recent confirmation of user identity.

This is not unique to AI.

AI agents make it more relevant because workflows can persist and evolve over time without constant direct user input.

A long-lived conversation should not automatically become a permanent privileged session.

Agent Sessions Should Not Become Permission Containers

Conversation state can hold preferences and context.

It should not silently become proof of privilege.

A model remembering that “the user is an administrator” is not equivalent to an authenticated application session proving that role.

Likewise, an earlier conversation saying that a sensitive action was approved should not necessarily authorize a later action in another context.

Authorization state belongs in systems designed to manage security state.

Natural-language memory belongs in the model context.

Mixing them creates fragile boundaries.

Credentials Should Stay Outside Model Context

Agents may need credentials to reach tools.

The model generally does not need to see the raw secrets.

Backend systems can hold credentials and perform authorized operations on the model’s behalf.

OWASP advises against storing credentials, authorization data or other critical security information inside system prompts and recommends separating such controls from the LLM.

This reduces the chance that prompt leakage or context exposure turns into credential theft.

The agent needs capability.

It does not necessarily need the underlying secret.

Authorization Metadata Should Not Be Treated as Secret Protection

An application may expose information about roles or permission structure to the AI.

That may be necessary for useful reasoning.

The architecture should not depend on this information remaining hidden.

OWASP’s System Prompt Leakage guidance points out that revealing role structures becomes dangerous primarily when applications rely on weak or model-enforced authorization rather than strong independent checks.

Strong authorization remains secure even when an attacker understands how the role model works.

Security should come from enforcement, not obscurity.

Auditability Is Part of Authorization

Authorization answers whether an action should happen.

Auditability helps organizations understand what happened afterward.

AI agents can create complicated sequences of actions, so logging needs enough context to reconstruct important events.

A useful audit trail may record:

the initiating user,

the acting agent,

the capability used,

the relevant authorization decision,

the affected resource,

and the resulting action.

NIST explicitly includes auditing and non-repudiation among the open questions in its AI agent identity and authorization project.

This becomes critical when organizations need to investigate unexpected autonomous behavior.

Audit Logs Should Distinguish User Intent From Agent Action

Imagine a user asks:

“Clean up outdated project information.”

The agent decides to delete several records.

A simple log might say:

User requested cleanup.

Another log might say:

AI service deleted records.

Neither alone tells the complete story.

A more useful record connects them.

User X initiated workflow Y.

Agent Z selected operation A.

Authorization policy B allowed the operation.

Objects C and D were affected.

This allows security teams to reconstruct both intent and execution.

Authorization Failures Should Be Observable

When an AI agent repeatedly attempts operations outside its permitted scope, that can be useful security information.

One denied action may be a harmless model mistake.

Repeated denied access to unrelated users’ records could indicate manipulation or incorrect agent logic.

Monitoring should therefore include significant authorization failures, not only successful tool calls.

This helps security teams identify when model behavior is repeatedly pressing against capability boundaries.

Rate Limiting Complements Authorization

Authorization determines whether an operation is allowed.

Rate limiting determines how much activity can happen over time.

OWASP recommends rate limiting and monitoring as ways to reduce potential damage from undesirable agent actions even when they do not directly prevent the underlying Excessive Agency weakness.

For high-impact operations, limiting frequency can reduce blast radius.

A compromised agent capable of one approved operation may be dangerous.

The same agent capable of repeating the operation thousands of times autonomously can be far more damaging.

Fail Closed When Authorization Context Is Missing

AI workflows frequently cross several services.

Sometimes identity context can become unavailable.

A secure default is generally to avoid converting missing authorization context into broad service-account authority.

If the backend cannot determine which user or agent is responsible for a sensitive action, granting the maximum available permission is a dangerous fallback.

The correct handling depends on architecture, but the principle should remain conservative.

Uncertainty about authority should not become increased authority.

Avoid Building One Super-Agent

A universal AI agent with access to every business capability is attractive from a product perspective.

It creates a large security boundary.

OWASP’s guidance recommends limiting extensions and extension functionality to the minimum necessary, and its System Prompt Leakage guidance notes that tasks requiring different access levels may benefit from separation into agents with appropriately constrained privileges.

This supports a compartmentalized architecture.

A research agent does research.

A support agent handles support workflows.

A deployment agent handles deployment under stronger authorization.

Compromise or manipulation of one component then has a more limited blast radius.

Specialized Agents Can Create Cleaner Permission Models

Specialization helps security because permissions can match purpose.

An agent that summarizes documents can receive read access.

An agent that drafts communications can create drafts but not send them.

An execution agent can operate only after explicit authorization.

Each security boundary becomes easier to reason about.

This is less flexible than giving one universal agent every capability.

It is substantially easier to test.

How to Penetration Test AI Agent Authorization

AI agent security testing should begin by mapping the complete identity and permission chain.

Researchers need to understand the users, roles, agent identities, service accounts, tools and downstream systems involved.

The first objective is not necessarily to manipulate the model.

It is to understand where authority comes from.

Which identity authenticates to each service?

Which permissions belong to that identity?

Can the agent access more than the initiating user?

Does backend authorization know which user started the action?

Are high-impact tools always available?

Does permission change according to role?

Once that map exists, adversarial testing becomes much more meaningful.

Test the Same Agent With Different User Roles

Role comparison is one of the highest-value tests.

The same agent should behave differently when operating under different legitimate permission sets.

An ordinary user should not gain administrator-level actions because the backend uses a shared service identity.

A customer should not reach another tenant’s records.

A read-only employee should not gain write capability through the AI interface.

The researcher should compare both tool visibility and actual downstream execution.

A hidden tool may still be reachable.

A visible tool may correctly reject unauthorized operations.

Runtime enforcement matters more than interface appearance.

Test What Happens When the Model Requests Too Much

An AI security assessment should deliberately evaluate whether authorization survives unexpected model behavior.

The researcher can test controlled scenarios where the model attempts an operation beyond the current user’s authority.

The strongest outcome is not necessarily that the model refuses.

The strongest outcome is that the backend refuses even if the model requests the action.

That proves the security boundary does not depend solely on probabilistic model behavior.

Test Object-Level Authorization Through the Agent

If the agent can access objects by identifier, researchers should verify ownership and tenant boundaries.

Can a user request another user’s record indirectly through natural language?

Can the model infer or supply a different object identifier?

Does the downstream tool validate ownership?

Does retrieval expose metadata before authorization?

These tests should mirror conventional API access-control testing.

The AI interface is simply another route into the same security-sensitive objects.

Test Delegated Identity

Where the agent acts on behalf of the user, the researcher should determine whether delegation preserves user restrictions.

Does the agent use an OAuth-style delegated identity?

A backend service credential?

A custom policy layer?

Does the downstream system know the original actor?

Could one user cause the agent to use authority associated with another?

The exact technology matters less than the resulting security property.

Delegation should not silently produce privilege escalation.

Test Agent-to-Agent Authorization

Multi-agent systems deserve separate tests.

Can a low-privilege agent instruct a higher-privilege agent to perform sensitive work?

Does Agent B independently validate the authority associated with Agent A’s request?

Can natural-language claims about user approval bypass real authorization?

Can one compromised agent cause another to exceed its intended role?

These scenarios become more relevant as organizations adopt complex agentic workflows.

Test Revocation

Permissions change.

Employees leave.

Roles change.

Access tokens expire.

A user may revoke access to an external integration.

Agent authorization needs to reflect those changes promptly.

Security testing should examine whether revoked permissions remain usable through cached agent state, persistent tool sessions or long-running workflows.

A revoked user’s old conversation should not preserve authority that the current identity system has removed.

Test Long-Running Agent Workflows

Agents may continue working beyond one immediate request.

That means the authority valid when the workflow started may no longer be valid when a later action executes.

A long-running process should not necessarily assume authorization state remains unchanged forever.

High-impact operations may need re-evaluation at execution time.

This is especially important when workflows involve approvals, changing roles or external services.

Test Approval Boundaries

Where human confirmation is required, researchers should verify that it cannot be bypassed.

Does the backend require proof that approval occurred?

Or does the interface merely show a confirmation message while the agent can still call the underlying tool directly?

Can the agent alter the target after approval?

Does approval for one object unintentionally cover another?

Human approval becomes meaningful only when it is bound to the operation actually executed.

Test Credential Scope

Researchers should inspect which credentials tools use downstream.

A read-only AI feature connected with administrator-level database access deserves scrutiny even if no exploit has yet been demonstrated.

Security testing should determine whether narrower permissions can preserve functionality.

This is particularly important because excessive credential authority significantly increases the consequences of prompt injection or model mistakes.

Test Authorization After Prompt Injection

Prompt-injection testing becomes far more valuable when connected to real permission boundaries.

If indirect content manipulates an agent, the researcher should determine whether that manipulation creates authority.

Can it access another user’s data?

Can it call a privileged tool?

Can it change application state?

If backend authorization prevents these outcomes, an important defense layer is working.

If prompt manipulation can translate directly into privileged action, the architecture needs stronger separation.

Severity Should Depend on Authority Gained

Not every agent-authentication weakness has the same impact.

A minor logging ambiguity differs from global cross-tenant access.

An agent with unnecessary read access differs from one capable of changing production security settings.

Severity should reflect the additional capability an attacker can gain.

The key question is:

What can this attacker do through the agent that they could not legitimately do without it?

That question turns abstract AI behavior into practical security impact.

Remediation Should Restore the Permission Boundary

When an agent authorization weakness is discovered, remediation should focus on the broken security property.

If a shared service account bypasses user-level authorization, restore user context or enforce equivalent downstream policy.

If an agent has unnecessary write access, reduce its permission.

If high-impact operations bypass approval, move the approval into the actual execution path.

If permission rules exist only in a prompt, implement them in deterministic code.

If a multi-agent system trusts natural-language claims of authorization, introduce authenticated delegation.

The fix should make the unauthorized outcome technically impossible rather than merely less likely.

Retesting Should Try Different Model Behavior

A weak remediation changes the system prompt so the original attack wording no longer works.

A strong remediation fixes authorization.

Retesting should therefore attempt alternate paths to the same restricted operation.

If the user still lacks permission regardless of how the model phrases or selects the action, the boundary is much stronger.

This is particularly important because model behavior is probabilistic.

Authorization should not be.

Designing Safe Permission Boundaries Before Launch

The easiest authorization vulnerability to fix is the one prevented during architecture design.

Before deploying an agent, product teams should map what it can do.

Which data can it read?

Which records can it modify?

Which tools can it call?

Which service accounts does it use?

Does the user’s identity survive into downstream systems?

Which actions require stronger permission?

Which operations require explicit approval?

Can the agent act without a human present?

What happens if prompt injection succeeds?

This threat-modeling process is substantially cheaper than trying to reconstruct authorization after a privileged agent is already in production.

Start With the Human User

For user-facing agents, a strong security model often begins with the human’s existing permissions.

Ask what the user could do through the normal application.

Then consider whether the agent needs anything beyond that authority.

In many cases, it does not.

The agent changes the interface and automation level.

It should not silently change the user’s permission level.

Then Define the Agent’s Own Authority

Some capabilities belong to the agent itself.

Perhaps it can create temporary analysis artifacts.

Perhaps it can read a shared documentation index.

Perhaps it can query an internal service used by every user.

These capabilities should be explicit.

Security teams should be able to describe them independently of any one conversation.

An agent whose authority cannot be clearly described is difficult to secure.

Then Define Every Tool Boundary

Each tool should have a specific security contract.

What does it do?

Which identities can call it?

Which objects can it affect?

Does it read or write?

Which backend credential does it use?

Does it require human approval?

Can it communicate externally?

What does it log?

This makes the agent architecture testable.

Finally, Assume the Model Makes the Wrong Decision

This is the most useful architecture test.

Assume the model chooses the wrong tool.

Assume prompt injection succeeds.

Assume untrusted content alters its reasoning.

Assume the user asks an ambiguous question.

Assume the agent hallucinates a dangerous action.

What happens next?

If the answer is:

“The model would probably refuse,”

the security boundary is weak.

If the answer is:

“The backend rejects any operation outside the authenticated user’s authorization,”

the architecture is much stronger.

OWASP’s guidance around Excessive Agency and System Prompt Leakage reflects this exact principle: constrain capabilities, enforce permissions outside the model and maintain deterministic authorization boundaries around sensitive actions.

Authentication and Authorization Are the Foundation of Agentic AI Security

AI agents create new security challenges because they introduce reasoning and autonomy into workflows that used to be deterministic.

But the strongest defenses are not entirely new.

Know the actor.

Preserve identity.

Limit permission.

Authorize every sensitive action.

Keep user and agent authority distinct.

Use least privilege.

Separate read from write capability.

Require stronger controls for high-impact operations.

Do not store critical authorization rules only in prompts.

Keep secrets outside model context.

Log important actions.

Retest permission boundaries after remediation.

NIST’s dedicated 2026 work on agent identity shows that these issues are moving from theoretical discussion into a central standards and enterprise-security problem.

The most important principle remains straightforward:

AI can decide what action to request. It should never decide, by itself, whether that action is authorized.

Frequently Asked Questions About AI Agent Authentication and Authorization

What is AI agent authentication?

AI agent authentication is the process of establishing the identity of a user, software agent or autonomous AI actor participating in a workflow. Modern agent systems may involve several identities at once, including the human user, the agent itself and backend service identities. NIST is actively developing work around how existing identity standards and best practices should apply to AI agents.

What is AI agent authorization?

AI agent authorization determines which data, tools, objects and actions an agent may access under a given identity and context. Authorization should be enforced by deterministic application or downstream security controls rather than relying on the LLM to decide whether an action is allowed.

Should an AI agent have its own identity?

In many architectures, yes. A distinct agent identity can improve control and auditability, while the original user identity still establishes on whose behalf the agent is acting. NIST’s current agent-security work explicitly includes identification, authorization, auditing and non-repudiation.

Can an AI agent use a service account?

It can, but broad shared service accounts can create privilege problems if user-level authorization is lost. OWASP warns against extensions operating for individual users through generic high-privilege identities and recommends preserving user security context with minimum necessary permissions.

Should authorization rules be placed in the system prompt?

Security-critical authorization should not depend on system prompts. OWASP recommends enforcing privilege separation and authorization bounds independently from the LLM because deterministic and auditable security controls are more appropriate for access decisions.

How does least privilege apply to AI agents?

Agents should receive only the tools, functions and downstream permissions necessary for their intended tasks. OWASP recommends minimizing extensions, extension functionality and extension permissions to reduce the impact of unexpected or manipulated model behavior.

Does human approval replace authorization?

No. Authorization determines whether a user or agent has permission to perform an operation. Human approval confirms intent for an operation that is already authorized. OWASP recommends human approval for high-impact actions as an additional control rather than a replacement for downstream authorization.

How should AI agent permissions be tested?

Security testing should map users, agents, tools and downstream identities, compare multiple roles, test object and tenant isolation, validate credential scope, examine delegated authority and determine whether unexpected model behavior can cross a real permission boundary.

Can prompt injection bypass AI agent permissions?

Prompt injection can influence what an agent attempts to do, but strong downstream authorization should prevent manipulated model behavior from creating new privileges. OWASP recommends least privilege and external authorization controls specifically to reduce this risk.

What is the biggest authorization mistake in AI agent architecture?

One of the most dangerous patterns is allowing low-privilege users to interact with an agent that performs downstream actions using a generic highly privileged identity without re-enforcing the original user’s authorization. OWASP identifies this as a form of excessive permission within Excessive Agency.