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What changes when automation becomes an agent?
Both systems depend on the security of their software, infrastructure, identities, data, and connected services. Confidentiality, integrity, and availability remain core concerns. The distinction is the decision path: a conventional workflow often runs a configured sequence or ruleset, while an AI agent may interpret natural-language instructions and choose steps or tools in response to context.
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That can expose an agent to instructions embedded in material it reads, such as a web page, email, or document. It may also maintain conversational or persistent memory and select or chain actions at runtime. These are additional security considerations, not proof that every agent is less secure than every automated workflow. NIST describes AI security as building on traditional software concerns while addressing AI-specific risks in its Security and Resilience research. OWASP’s AI Agent Security Cheat Sheet describes agents as systems that can reason, plan, use tools, maintain memory, and take actions to accomplish goals.
Security differences at a glance
| Dimension | Traditional automation | AI agent security consideration |
|---|---|---|
| Decision path | Often a configured sequence or ruleset. | May interpret instructions and choose steps or tools based on context. |
| Inputs | Forms, events, APIs, files, and application data. | The same inputs, potentially including untrusted text from web pages, documents, or email that could influence instructions. |
| Authority | Uses a configured service identity and its permissions. | Tool permissions must be scoped; model output must not grant itself authority. |
| State | Application state, logs, queues, or databases. | Those states plus conversational context or persistent memory that may contain sensitive or poisoned content. |
| Execution | Runs defined actions subject to application controls. | May select or chain actions at runtime, creating opportunities for tool misuse, goal hijacking, and cascading failures. |
| Oversight | Change management, access review, monitoring, and rollback. | Retain those controls; consider risk-based approval, action previews, interruption or rollback where feasible, and structured records of tool calls and decisions. |
| Testing | Functional, security, and abuse-case testing. | Also test prompt injection, tool misuse, memory poisoning, data exposure, identity boundaries, multi-agent communication, and cost or retry loops. |
This is a practical synthesis of NIST and OWASP guidance, not a claim that every system in either category has the same architecture.
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Where agent-specific risks arise
Prompt injection and goal hijacking
Direct instructions from a user or indirect instructions embedded in external content can influence an agent’s behavior. The risk is not limited to obviously malicious prompts: an agent may treat text it was meant to summarize or analyze as directions for its own next steps. OWASP identifies direct and indirect prompt injection and goal hijacking among agent risks in its security guidance.
Tool abuse and excessive privilege
An agent can misuse a tool that has broader access than the task requires. A request to retrieve information should not implicitly give the agent permission to edit records, send messages, or administer an account. Grant the minimum necessary tools and resource permissions, and enforce access in the tool or execution layer rather than relying on the model to judge whether an action is allowed.
Sensitive data exposure
Information can enter an agent’s context or leave through its response, a tool call, an API, or logs. Apply data classification and protection to the full path. Validate outputs before displaying them or using them to trigger actions; a plausible-sounding answer is not a security check.
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Memory poisoning and cross-session leakage
Persistent or shared memory can carry malicious instructions or sensitive information into later interactions. Treat memory as data that needs access controls and validation: isolate it by user or session, screen for sensitive content, check what is stored, and set retention limits.
Harmful actions and cascading failures
An action can be valid under a tool’s technical permissions and still be harmful in context. Autonomy therefore needs to reflect impact, not just whether an action is technically available. In a multi-agent setup, one compromised or manipulated agent may influence another through delegation or shared information; treat inter-agent communication as a security boundary.
Cost exhaustion and supply-chain exposure
Unbounded loops, retries, or tool chains can consume resources. Third-party tools, APIs, and data sources also create dependency risks. Set limits for tokens, costs, retries, and tool chains, and assess the security of connected dependencies. OWASP discusses these risks in its AI Agent Security Cheat Sheet.
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Controls that matter most
1. Inventory the agent and its authority
Record each agent’s purpose, owner, identity, data access, connected tools, and ability to act across systems. This inventory gives security teams a way to review where the agent can read, change, or transmit information. NIST’s AI Agent Standards Initiative, created in 2026, describes work on standards, protocols, authentication and identity infrastructure, and evaluations; it is an initiative, not a completed agent-security standard.
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Provide only the tools needed for the task and scope them by operation and resource. Separate read and write permissions where possible. A model’s decision to call a tool should not determine whether that call is authorized.
3. Make an independent authorization check before execution
An execution component or policy service should validate the actor, target, parameters, privilege, and any required approval immediately before a consequential action. This makes the permission decision independently enforceable, even if the model has been influenced by malicious content or produces an unsafe plan.
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4. Treat external content and tool output as untrusted
Constrain and validate user input, retrieved content, and results returned by tools. A website, email, or document does not become trustworthy because an agent can read it. Keep the boundary between information the agent is analyzing and instructions it is allowed to follow.
5. Protect context and memory
Isolate memory by user or session, validate content before persistence, screen for sensitive data, and apply retention limits. These controls reduce the chance that one interaction contaminates another or that confidential information is retained unnecessarily.
6. Require risk-based human approval
Use explicit approval for high-impact, irreversible, financial, administrative, or externally visible operations. Show a preview and bind approval to the exact action and parameters; an approval for a general goal should not authorize a materially different action. Fail closed if policy or approval checks fail.
7. Monitor tool use and test the whole system
Keep structured records of decisions, tool calls, outcomes, and policy results, and alert on unusual tool use. Test the integrated system—not only its model or application code—for prompt injection, privilege boundaries, data exposure, memory poisoning, multi-agent communication, and cost or retry loops. Repeat adversarial testing after relevant changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to use NIST and OWASP guidance
NIST’s AI Risk Management Framework (AI RMF) is voluntary guidance for incorporating trustworthiness considerations into AI design, development, use, and evaluation. Its functions—Govern, Map, Measure, and Manage—can organize responsibilities, context, evaluation, and mitigations. NIST says AI RMF 1.0 is being revised, so check the current framework page when citing its status.
Other NIST work has a different scope. The AI Research – Security and Resilience page describes planned Control Overlays for Securing AI Systems, including proposed single-agent and multi-agent use cases built on established security controls and AI resources; these are work in development, not a completed agent-specific standard. NIST’s AI 100-2 E2025, published March 24, 2025, provides adversarial machine-learning taxonomy and terminology. It is useful background for attack vocabulary, not a full operational control standard for agents.
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OWASP’s cheat sheet is practical project guidance, not a regulation or certification. Its December 2025 Agentic Applications Top 10 announcement reports input from over 100 security researchers, practitioners, user organizations, and technology providers. That contributor count describes input to the project; it is not an incident-rate, prevalence, or effectiveness statistic.
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