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Secure an AI agent by treating every prompt, retrieved document, tool result, and memory entry as untrusted; limiting its tools and delegated authority; and checking consequential actions outside the model before they execute. An ordinary language-model app gives a person text to consider. An agent can use credentials and tools to act, so a prompt-injection failure can cross from a misleading answer into an unauthorized operation.
Why agents change the security problem
A conventional language-model application generally returns text for a person to interpret and act on. An agent can plan steps, call tools, retain state, and interact with other systems. Its actions may use its own identity or credentials delegated from a user. That creates additional trust boundaries: the agent’s instructions, the content it reads, its memory, its tool interface, and the systems that ultimately execute its requests.
Prompt injection is therefore not only a risk to answer quality. An attacker may hide instructions in a document, web page, API response, or other content that the agent later retrieves. If the agent treats that content as authoritative instructions, it may use an available tool to disclose data, alter a record, or take another unintended action. NIST’s CAISI describes this class of indirect prompt injection as agent hijacking.
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Build trust boundaries around every input
Do not treat content as trusted merely because it arrived through an expected channel or was returned by an approved tool. A legitimate search result or API response can contain attacker-controlled text. Apply the same caution to conversation history and messages passed between agents.
- Separate instructions from data. Make clear in the application design which content defines policy and which content is material to analyze. Do not let retrieved text or tool output silently become higher-priority instructions.
- Mark and handle untrusted content consistently. User prompts, retrieved documents, API responses, tool results, prior conversation, and inter-agent messages can all carry hostile or misleading instructions.
- Validate at boundaries. Check data entering the agent, arguments leaving it for tools, and results returned by tools. Apply schema, type, range, and allowlist checks appropriate to each operation.
- Keep execution separate from interpretation. The component that executes a consequential action should apply authorization policy itself rather than relying on the model’s account of why an action is safe.
These measures reduce the opportunity for untrusted content to redirect behavior, but they do not make the model’s interpretation reliably immune to injection. Design on the assumption that an attack may influence what the model proposes.
Limit tools, permissions, and delegated identity
Give an agent only the capabilities required for its specific task. A broad toolbox granted “just in case” turns a model error or successful injection into a larger incident. The model’s confidence, stated intent, or decision to call a tool is not an authorization decision.
Scope capabilities to the job
- Expose task-specific tools instead of general-purpose access where possible.
- Separate read and write access. An agent that summarizes records may need read access without permission to change or delete them.
- Restrict operations and resources: for example, authorize an operation only on the relevant account, project, or record.
- Validate arguments against explicit schemas, allowlists, and sensible ranges before execution.
- Enforce authorization in the execution layer, including the actor, operation, target, and parameters.
Know whose authority the agent uses
For every downstream action, establish whether the request uses the agent’s own identity or credentials delegated from a user, and determine whether that authority is narrower than the user’s. Avoid standing, broadly scoped credentials when narrower access can serve the task. Check authorization for each action rather than assuming that a valid credential makes every requested operation appropriate.
NIST NCCoE’s concept paper announcement of 2026-02-05 describes proposed work on software-agent identity and authorization. It is an announcement of a potential project and consultation, not a completed agent-identity standard.
Put an independent approval gate around consequential actions
Require a reliable human approval step before actions that are externally visible, high-impact, or difficult to reverse. Examples include sending messages, deleting data, making purchases, changing permissions, and deploying changes. The approval check should happen in an execution or policy layer independent of the model’s claim that a person approved.
Bind approval to the exact action: the actor, tool, target, and parameters. If any of those change, the earlier approval must not authorize the altered call. Fail closed if approval cannot be validated. A Boolean flag such as user_confirmed is insufficient on its own; OWASP’s AI Agent Security Cheat Sheet cautions that approval must be tied to the current actor and exact tool call, then consumed atomically so it cannot be reused or raced.
Approval is not a substitute for least privilege. Keep the agent’s capabilities limited even when a workflow includes a human check, and make the proposed action legible enough for the reviewer to understand what will happen and to which target.
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Protect memory, logs, and sensitive data
Persistent memory can carry an attack or sensitive information beyond the turn in which it entered the system. Treat memory as a data store with access controls and lifecycle rules, not as harmless context.
- Isolate memory between users and sessions; do not allow one user’s stored context to leak into another user’s agent.
- Validate and sanitize entries before saving, and retain provenance so the application can distinguish where information came from.
- Set expiration and size limits. Persist only information the application needs, and review stored content for sensitive data.
- Protect conversation traces and function-call results. Logs can contain personally identifiable information, secrets, or sensitive tool output, so minimize what is collected and secure what is retained.
Apply comparable controls to short-term conversation state and to any shared memory used across agents. A message from another agent is still content crossing a trust boundary, not automatically a trusted instruction.
Contain runaway behavior and monitor actions
Agents can repeat tool calls, create long chains of work, or consume resources unexpectedly. Set explicit ceilings for steps, iterations, retries, tokens, and cost; detect loops; and restrict which tools can be chained together. Where appropriate, constrain network egress and isolate execution so that a compromised or misdirected agent has fewer ways to reach external systems.
Keep structured audit records for consequential actions, including the actor, requested operation, target, parameters, authorization result, and approval outcome. Design those records to support investigation without retaining unnecessary sensitive content. Monitoring should surface unusual action patterns and repeated failures, not merely capture a transcript after the fact.
Test the application as a system
Testing only the model’s answers misses failures in orchestration, tools, identity, memory, and approval handling. Build repeatable abuse cases that exercise the complete path from attacker-controlled content to attempted action.
- Indirect prompt injection in retrieved documents, API responses, and tool output.
- Unauthorized tool use, argument manipulation, and attempts to exceed the agent’s resource scope.
- Privilege escalation or misuse of delegated credentials.
- Memory poisoning, cross-user or cross-session leakage, and sensitive-data exposure.
- Approval bypass, approval reuse, and changes to a tool call after approval.
- Recursive tool chains, loops, resource exhaustion, and multi-agent trust-boundary failures.
Evaluate performance on the actual tasks the system is intended to perform, including adaptive attacks and multiple attempts. NIST CAISI’s 2025-01-17 discussion of agent-hijacking evaluation highlights task-specific attack performance, adaptive testing, and testing across multiple attempts. A single successful run or a single blocked attack is not enough to characterize behavior.
Repeat relevant tests whenever prompts, tools, retrieval sources, memory behavior, policies, orchestration, or model providers change. OWASP recommends repeatable abuse-case testing and release gates in the development pipeline; treat a security regression as a release concern, not only as a post-deployment monitoring issue.
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Choose controls that fit the deployment model
Responsibility shifts as an organization moves from a managed agent service toward building and operating more of the stack itself. Microsoft’s shared-responsibility model is illustrative, not a universal contract or legal rule; the exact division depends on the service and configuration.
Best Value
| Deployment | Customer control and responsibility | Customization | Operational burden | Visibility and governance |
|---|---|---|---|---|
| SaaS agent | The vendor operates most of the platform; the customer still configures data access and identity and remains responsible for important application-level decisions. | More bounded by the managed service than in a customer-built stack. | Lower platform-operating responsibility than PaaS or IaaS in Microsoft’s illustrative model. | Establish customer ownership for data scope, identity, authorization, oversight, and acceptable use; the model does not specify a universal visibility level. |
| PaaS agent | The customer also owns more of the instructions, tools, permissions, orchestration, memory, and identity. | More of the agent’s behavior and components are under customer configuration. | Greater than SaaS because more components and controls are customer-managed. | Govern the added customer-managed instructions, tools, permissions, orchestration, memory, and identity; the model does not specify a universal visibility level. |
| IaaS agent | The customer owns nearly the whole stack in Microsoft’s illustrative model. | Most of the stack is under customer control. | Highest customer operating responsibility of these three categories. | Assign governance and operational ownership across the stack; the model does not specify a universal visibility level. |
These categories do not by themselves establish security quality: a managed service still needs carefully scoped data and identity, while a self-hosted system still needs authorization, oversight, and testing. For any deployment, name an accountable owner for data access, identity, action authorization, human oversight, and governance. Microsoft’s shared-responsibility page was last updated 2026-08-26.
Use a layered control plan
A useful way to check coverage is to group controls by what they do. These groupings organize documented practices; they are not a formal taxonomy attributed to one source.
| Control role | Examples | What it addresses |
|---|---|---|
| Preventive | Least privilege, task-specific tool allowlists, argument validation, independent authorization checks, approval gates | Blocks or challenges an unsafe action before execution. |
| Containment | Sandboxing, egress limits, restricted tool chaining, step and cost ceilings | Limits the reach and impact of a failure or runaway process. |
| Detective | Structured action logs, monitoring, abuse-case testing, adversarial evaluation | Helps identify misuse, regressions, and weaknesses that preventive controls did not stop. |
Microsoft Learn’s Agent Safety guidance, last updated 2026-08-25, states: “Building secure AI agents is a shared responsibility between Agent Framework and application developers.” In practice, the application team must make the boundaries and enforcement explicit rather than relying on a model or platform to infer the organization’s risk tolerance.
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