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Security leaders should secure AI agents according to the data they can reach, the identities they use, and the actions they are allowed to take—not according to the most dramatic attack scenario. Inventory deployed agents, limit their permissions, test how they handle hostile inputs and tool calls, log their actions, and require human approval for consequential or hard-to-reverse operations.
What makes an AI agent a security concern?
An agent combines a model with software functions that can plan and take actions. That combination matters: a model output may cause a tool to retrieve data, change a record, send a message, or affect another system. The security exposure therefore depends not just on the model, but on its inputs, tools, identity, permissions, and oversight.
NIST’s Center for AI Standards and Innovation described agent systems as capable of planning and taking autonomous actions that affect real-world systems or environments in its January 2026 request for information on securing AI agent systems. The request identified risks arising when model outputs are combined with software functionality. These include indirect prompt injection through adversarial data, compromised or insecure models such as those affected by data poisoning, and harmful actions without an adversary, such as specification gaming or misaligned objectives.
These are risks to assess, not evidence that spectacular agent attacks are widespread or imminent. The available sources do not establish a comparative likelihood ranking between dramatic autonomous attacks and conventional security threats, or a reliable frequency measure for agent-specific attacks.
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Which risks belong in an assessment?
Use threat categories to ask concrete questions about your own systems. OWASP’s Agentic AI Threats and Mitigations guide and Agentic Threats Navigator organize attack surfaces across reasoning, memory, tools, identity, human oversight, and interactions among agents. They are assessment frameworks, not frequency statistics.
- Adversarial inputs: Can instructions embedded in web pages, documents, email, or other untrusted content steer the agent away from its intended task?
- Tool misuse: Could the agent use a legitimate tool in an unsafe way, or exploit weaknesses in the tool or the application it connects to?
- Identity and privilege abuse: Does the agent act through an identity with broader access than its task requires, or can its credentials be misused?
- Memory and persistence: What information persists between tasks, who can change it, and could poisoned or inappropriate memory affect later actions?
- Misaligned or underspecified objectives: Could an agent satisfy a goal in a way that technically follows its specification but causes an unwanted result?
- Delegation and oversight: Can agents pass work or authority to other agents, and is there a person or control point able to detect and stop consequential actions?
OWASP’s 2026 Top 10 for Agentic Applications highlights behavior hijacking, tool misuse and exploitation, and identity and privilege abuse. More than 100 security researchers, practitioners, user organizations, and technology providers contributed input to that list, according to OWASP’s announcement. That is a contributor count, not evidence of how often those attacks occur.
How should leaders compare agent deployments?
Compare deployments by exposure and authority rather than by labels such as “agentic” or by a frightening scenario in isolation. A useful review records the following for each agent:
- Data: What information can it read or change, and how sensitive is that information?
- Inputs: Can it consume external or untrusted content, and where can that content enter its workflow?
- Tools and permissions: Which applications and operations can it invoke? Can it write, delete, send, approve, or execute?
- Identity: Which identity does it use, who owns it, and how narrowly are its privileges scoped?
- Persistence: Does it retain memory or state across sessions, and what controls govern that persistence?
- Delegation: Can it invoke other agents or pass tasks, data, or authority onward?
- Impact and reversibility: What happens if an action is wrong, and can it be undone?
- Observability and approval: Are inputs, tool calls, decisions, and outcomes logged? Which actions require human approval?
This comparison helps distinguish a tightly bounded assistant from a system that can access sensitive information, persist state, and take consequential actions. Those differences should drive the level of testing, monitoring, and approval required.
What controls should a security program adapt?
NIST’s May 2026 summary of responses to its RFI reports broad agreement that fundamental cybersecurity practices remain relevant but need adaptation for agent security. In practical terms, extend established controls to cover agents’ ability to act, invoke tools, retain information, and use delegated authority.
Build an inventory and map authority
Record each agent’s owner, purpose, deployment location, connected tools, data access, identity, permissions, persistence, and approval path. Include agents embedded in applications as well as those built or configured directly by internal teams. The inventory is useful only if it reveals what an agent can actually do, not just its name or model.
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Constrain identities and permissions
Give agents distinct, attributable identities where the architecture permits, and scope access to the task and resources they need. Separate read access from write or execution privileges. Review delegated permissions and credential handling, and avoid allowing an agent to inherit broad human or service-account access by default.
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Test the whole workflow
Test beyond the model’s direct responses. Include indirect prompt injection through the kinds of external content the agent processes, attempts to misuse available tools, unexpected or conflicting instructions, and unsafe outcomes caused by ambiguous objectives. Assess whether tool permissions, application controls, and workflow design prevent harmful actions even when the model behaves unexpectedly.
Monitor actions and preserve evidence
Log the agent’s identity, relevant inputs, tool calls, permission decisions, approvals, and resulting changes in a way that supports investigation. Monitoring should make unusual access or action patterns visible and provide a means to pause or disable an agent when needed. Logging model text alone will not show which external actions occurred.
Put human review at consequential boundaries
Require approval when an action could materially affect people, sensitive data, finances, access rights, or important systems—especially when it is difficult to reverse. Human review should occur before the action, not merely as a post-event notification. For lower-impact, reversible tasks, controls can be proportionate to the deployment’s exposure and the quality of its monitoring.
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How can leaders avoid overreacting to sensational scenarios?
Use threat frameworks to structure questions, not to claim that every listed failure is common. OWASP’s threat materials and Top 10 provide vocabulary for reviewing agent systems; they do not establish incident prevalence. Likewise, the NIST materials identify risks and control themes rather than predicting which attack will occur next.
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Prioritize work by combining plausible pathways with actual access and impact. An agent that processes untrusted content but cannot change anything presents a different exposure from one that can alter production records or send external communications. The comparison should account for privilege, persistence, delegation, reversibility, and the chance to detect and stop an action—not only for how dramatic a hypothetical scenario sounds.
OWASP’s Q2 2026 AI Security Solutions Landscape maps open-source and commercial tools to agent-security tasks and lifecycle stages, and OWASP says it is updated quarterly. It can help orient a tool review, but any tool should be checked against the current landscape and evaluated against the organization’s architecture. The landscape is not a substitute for defining the threat model and controls first.
For governance context, OWASP’s June 2026 State of Agentic AI Security and Governance 2.01 reviews governance models and regulatory standards. It does not, by itself, establish jurisdiction-specific legal obligations or implementation requirements.
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