AI agents add a model-driven layer to automation: instead of following only code-defined steps, an agent may interpret a goal, choose actions from context, and call tools. That can make a system more flexible, but it also creates more ways for untrusted input to influence what happens. The practical security question is what the system can do, what can steer it, and which controls still hold if its model makes a bad decision.
What makes an AI agent different from traditional automation?
Traditional automation generally runs configured triggers and code-defined branches. An AI agent may interpret a goal, plan steps, use tools, maintain memory, and act with limited human supervision. NIST’s National Cybersecurity Center of Excellence (NCCoE) describes agents as systems capable of autonomous decision-making and action toward complex goals; OWASP likewise describes reasoning, planning, tool use, memory, and action as common agent capabilities.
The distinction is about architecture, not product labels. A workflow may use fixed code for some steps and a model to choose or draft others. Assess which components select actions, which inputs influence those choices, and what authority the resulting actions have. Automation is not automatically safe because it is scripted, and an agent is not automatically unsafe because it uses a model.
How do security and control differ?
| Security area | Traditional automation | AI agent deployment | What to examine |
|---|---|---|---|
| Action selection | Usually follows configured triggers, code-defined conditions, and workflow branches. | May select and sequence tool calls based on a goal, model output, and task context. | Can actions be enumerated, bounded, and replayed? Which choices come from code and which from the model? |
| Input trust | Workflow data can still exploit ordinary software flaws or manipulate decisions made by the workflow. | Documents, web pages, emails, and other task data may contain text that the agent interprets as instructions. | Are trusted instructions separated from untrusted content? Are consequential actions independently checked? |
| Identity and access | Service accounts and application permissions remain important control points. | Agent identity, delegated access, credentials, tool scopes, and attribution need to be explicit. | Is there a unique identity, task-bounded entitlement, least privilege, revocation path, and audit trail? |
| Human oversight | Approvals can be placed at defined workflow gates. | Human review may be needed for high-impact actions, but repeated prompts can lead to approval fatigue. | Does approval occur at a meaningful risk boundary, with the exact action visible? |
| Testing | Test workflow branches, application behavior, and conventional security cases. | Also test indirect prompt injection, tool misuse, data exfiltration, memory effects, and changing attack strategies. | Are abuse cases retested after model, tool, permission, or workflow changes? |
| Failure containment | Impact depends on the automation’s permissions and design. | Tool chaining and autonomous action can increase the potential blast radius. | Are execution environments constrained, tools narrow, actions limited, and activity monitored? |
These are comparison points, not guarantees about every deployment. Existing identity and access controls still apply; model-driven action selection adds risks that those controls must contain.
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How can untrusted input hijack an agent?
NIST calls a relevant attack pattern agent hijacking: malicious instructions hidden in data the agent consumes can redirect it toward harmful actions. For example, a seemingly ordinary email, document, or web page may include instructions intended for the agent. In some architectures, developer instructions and task-relevant data are combined in the model’s input, so text presented as data can influence behavior.
The risk becomes more serious when the agent can use tools or access sensitive information. A model instruction such as “ignore malicious text” is not a security boundary: it cannot guarantee that the model will reject every hostile instruction. Use permissions and execution controls that limit damage if the agent is manipulated, and validate high-impact actions independently. OWASP’s recommendations include input validation, tool authorization, and least privilege.
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What controls should an organization put in place?
Apply ordinary identity and authorization discipline, then add controls for the agent’s ability to select and chain actions. This is a practical synthesis of NIST and OWASP guidance, not a mandated sequence.
- Give the agent a distinct identity. Create agent-specific identifiers and credentials rather than sharing a person’s login. NIST security engineer Bill Fisher warns that shared credentials create accountability gaps and may bring security, privacy, and legal problems. Established patterns such as OAuth 2.0 and SPIFFE can inform enterprise identity designs, while agent-specific practices continue to develop.
- Delegate only task-scoped access. Limit what the agent can reach, for how long, and on whose behalf. Make entitlements revocable and ensure access decisions are enforced by the underlying systems, not just requested in natural language.
- Narrow tool permissions. Allow only the tools required for the task. Scope permissions per tool—for example, read rather than write, or access to specified resources only—and separate tool sets where trust levels differ. Require explicit authorization for sensitive operations.
- Constrain execution. Use sandboxing or other runtime boundaries, action limits, and constrained tool interfaces to restrict what can happen even if the model selects an unsafe step. Treat tool chaining as a potential route to greater impact, not as proof that each individual tool is safe.
- Validate actions and place approvals at risk boundaries. Require a human to approve consequential actions, such as an operation with significant external impact, and show the concrete action being authorized. Keep technical authorization in force whether or not a person approves; repeated low-value prompts can cause users to approve reflexively.
- Monitor and preserve an audit trail. Record the agent identity, relevant inputs, selected tool calls, authorization decisions, approvals, and outcomes in a way that supports investigation. Alert on unexpected access or action patterns and maintain a way to revoke access or stop execution.
- Test for abuse and adapt the tests. Include indirect prompt injection, tool misuse, exfiltration, memory effects, and attempts to evade known defenses. Reassess after changes to the model, tools, permissions, or workflow; passing a known test does not establish resistance to novel attacks.
What do agent-security evaluations show?
NIST’s Center for AI Standards and Innovation (CAISI) reported a bounded evaluation in 2025 using AgentDojo simulated environments and an upgraded Claude 3.5 Sonnet model. In one held-out Workspace test, the strongest newly developed attack succeeded 81% of the time, compared with 11% for the strongest baseline attack. Those figures describe that tested agent, task, and evaluation—not production agents generally or the likelihood of an incident in a particular organization.
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NIST’s 2026 CAISI announcement treats agent security as an ongoing research and guidance area. It identifies adversarial data, insecure models, specification gaming or misaligned objectives without adversarial inputs, and deployment interventions to constrain and monitor access. NIST’s NCCoE agent identity and authorization project page showed “Soliciting Comments” when accessed on October 4, 2026; its project status and guidance can change.
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When should you use an agent instead of a fixed workflow?
Prefer a deterministic workflow when the task and its decision rules can be specified reliably and flexibility brings little value. Consider agentic behavior when interpreting varied context or adapting steps is useful enough to justify additional testing and controls. In either case, evaluate the deployed system—not the label—including its inputs, identity, permissions, tools, approval points, and failure limits.
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