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AI agents add a model-driven decision layer to familiar software risks. They may interpret instructions and context, choose tools, and take actions across multiple steps; traditional rule-based automation usually follows programmed branches or workflow states. Neither is automatically safe. The practical security question is what each system can access, what it is allowed to do, and how well its actions are constrained and monitored.
How agent security differs from traditional automation
The distinction is a useful tendency, not a strict boundary: conventional automation can include machine learning, and an AI agent can be tightly constrained. Assess the system’s actual decision logic, tools, data, permissions, and approval points rather than relying on its label.
| Security dimension | Traditional rule-based automation | AI agent system | What to assess |
|---|---|---|---|
| How actions are selected | Typically follows explicit rules, programmed branches, or workflow states. | A model may interpret context, choose tools, and plan or revise actions. | Test the model and its surrounding software together, including the full action chain. |
| Inputs | Often uses structured or validated inputs, though it can still consume untrusted data. | May process natural-language instructions and content from documents, email, search, or tools. | Identify which content is trusted instruction and which is untrusted data; test whether retrieved content can change the task. |
| Authority | Often uses service accounts with workflow-specific permissions; misconfiguration remains possible. | May reach multiple tools, datasets, or applications and exercise that authority over a sequence of model-selected actions. | Define a distinct agent identity, narrow its access, and monitor what it can reach and do. |
| Failure behavior | Bugs and unexpected states can cause harm; controlled inputs and state may make failures reproducible. | Can also fail through model-driven decisions or pursue an unintended objective, even without an attacker exploiting a conventional software flaw. | Assess task-specific impact, changing inputs, repeat attempts, and escalation points. |
| Testing | Conventional software security testing remains important. | Needs conventional testing plus model- and agent-specific evaluations and red teaming. | Test the deployed workflow, and update evaluations as attacks and system components change. |
NIST’s January 2026 request for information (RFI) frames the distinct concern as the combination of model outputs and software functionality, while also noting overlap with software vulnerabilities such as authentication and memory-management flaws. That is why an agent should not be treated as just a script with a chat interface—or as a wholly autonomous system by default.
Which security risks deserve attention?
Indirect prompt injection and agent hijacking
An attacker may place instructions in a webpage, email, or file that an agent is asked to inspect. If the agent treats that content as trusted direction, it may be redirected toward an unauthorized action. NIST’s January 2025 agent-hijacking evaluation describes the underlying problem as a lack of clear separation between trusted internal instructions and untrusted external data in current LLM-agent architectures.
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Excessive authority and consequential tool use
A model’s mistaken or hijacked decision becomes more consequential when it can send email, run commands, read broad file stores, or change business systems. The risk depends not only on the model, but also on the actions its tools permit, the data those tools expose, and whether an action can be undone.
Data exposure and misuse of tools
A compromised workflow may expose information by sending it to an unauthorized destination. In NIST’s 2025 evaluation, simulated cloud-file exfiltration, code execution, and phishing through tools were among the tested task categories. These are experimental scenarios, not evidence that every agent has those capabilities or that these outcomes occur at a particular rate in deployed systems.
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Model, data, and dependency integrity
Insecure or poisoned models and data are part of the agent threat picture identified by NIST. Include the provenance and integrity of models, training or retrieval data, dependencies, and the software that connects them in the system’s security review.
Harm without a direct attack
An agent can cause harm by pursuing an objective in an unintended way, including through specification gaming or misaligned objectives. This means testing only for an attacker’s malicious prompt is not enough; assess whether ordinary task instructions and edge cases can lead to unsafe outcomes.
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Agents still depend on software, identities, data stores, and infrastructure. Authentication flaws, insecure memory handling, confidentiality, integrity, availability, and ordinary implementation vulnerabilities remain relevant. Keep standard secure-development, identity, and infrastructure controls in scope.
Controls that reduce agent risk
- Map the complete system boundary. Document the model, orchestration layer, tool interfaces, data sources, memory, identities, permissions, network egress, human approvals, and upstream model and data integrity dependencies. NIST’s voluntary AI Risk Management Framework (AI RMF) organizes risk work around Govern, Map, Measure, and Manage.
- Assign an identity and explicit authorization policy. Specify which agent is acting, on whose behalf, which resources it may reach, and which actions need separate approval. Use narrowly scoped permissions, and review them when the task, tools, or deployment changes. NIST’s February 2026 concept-paper announcement on identity and authority of software agents highlights identification and authorization as active issues; it describes proposed work, not a finalized mandatory standard.
- Constrain high-impact actions. Put consequential tools behind narrowly defined interfaces; validate arguments; restrict destinations and data scopes; and require approval for actions such as code execution, bulk export, payments, account changes, or external messages when their impact warrants it. Monitor access and keep records sufficient to reconstruct what the agent saw and did.
- Treat retrieved content as untrusted input. Separate or isolate external material from trusted instructions where possible. Test whether content from a webpage, file, email, or search result can override the task boundary. Filtering retrieved input can reduce exposure, but it is not a universal solution: evaluate it against new attacks and the tools available in the particular workflow.
- Keep conventional software security in place. Secure the agent framework, tools, identity providers, dependencies, host systems, and data stores using appropriate development and deployment practices. Agent-specific controls do not replace ordinary security engineering.
- Version and review the system over time. Track changes to prompts, models, tools, permissions, and evaluation results. Reassess after changes or newly observed attack patterns, because agent security risks and potential mitigations continue to evolve.
How to evaluate an agent’s exposure
Test the deployed workflow, not just the model or an integration in isolation. Build evaluations around the system’s actual business task, tools, data, and permissions. NIST’s CAISI evaluation, reported in its January 17, 2025 article and updated December 19, 2025, illustrates why both attack choice and retry count matter:
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- Attack strength varied substantially in one held-out Workspace evaluation. Against the tested upgraded Claude 3.5 Sonnet agent, the strongest novel red-team attack achieved an 81% attack success rate, compared with 11% for the strongest baseline attack. Those figures describe that model, framework, task sample, and attack setup—not the general likelihood that a deployed agent will be compromised.
- Repeated attempts changed the result across five specific hijacking tasks. Average attack success increased from 57% after one attempt to 80% when each attack was tried 25 times. This is an evaluation finding about retries, not a population-wide incidence estimate.
- Report results by task and consequence. A rate aggregated across tasks can conceal whether success means an innocuous action or data exfiltration, code execution, or an external message. Record task-specific outcomes, severity, and side effects, as well as aggregate results.
Repeat attacks when an attacker could reasonably retry, vary relevant inputs, and include adaptive red teaming tailored to the model and toolset. Success against known attacks does not establish resistance to new ones. The sources cited here do not establish a comparable population-wide incidence rate for vulnerable deployed agents.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical comparison checklist
When comparing an agent with an automation workflow—or reviewing two agent implementations—answer these questions for each system:
- How much discretion does the model or workflow have to select or revise actions?
- Which tools, data, accounts, and network destinations can it access?
- Which actions are irreversible, externally visible, or high impact?
- How are instructions distinguished from untrusted retrieved content, and what happens when the content conflicts with the task?
- Does the system have a distinct, reviewable identity and narrowly scoped permissions?
- Do monitoring and audit records cover the full action chain, including what the system saw and did?
- Which actions require human approval, and are those approval points placed before consequential actions?
- What do task-specific and repeated-attempt evaluations show, including their severity and side effects?
How established risk frameworks fit
NIST says fundamental cybersecurity practices remain relevant but need adaptation to address agent security. The voluntary AI RMF 1.0 provides a risk-management structure through Govern, Map, Measure, and Manage; it is being revised. NIST has also described proposed Control Overlays for Securing AI Systems covering single-agent and multi-agent systems and drawing on SP 800-53 and other resources. Proposed or draft materials are evolving guidance, not final requirements.
The key operational shift is to manage the model’s decision-making together with the software authority it can exercise. A workflow with narrow permissions, controlled tools, meaningful approval gates, and ongoing evaluation can limit the impact of a bad decision; one with broad access and weak monitoring can turn that decision into a serious security incident.
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