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AI agents do not make security risks disappear or replace them with an entirely new category. They inherit familiar weaknesses in software, identity and access management, and AI systems—but connecting a model to tools, credentials, data, and real-world actions can make those weaknesses more consequential. They also introduce or amplify challenges around adversarial inputs, delegation, and autonomous action. The practical question is not whether an agent is “safe” in the abstract; it is what it can access, what it can do, and what must happen before it does something high-impact.
Do AI agents create new security risks?
Some risks overlap with conventional software security. NIST notes that AI systems can have exploitable authentication or memory-management vulnerabilities, alongside concerns involving underlying software and hardware and the confidentiality, integrity, or availability of data. AI deployments also bring attack surfaces and abuses that existing frameworks do not yet comprehensively cover. NIST CAISI and NIST’s AI security overview therefore support a qualified version of the headline: many risks are familiar, but agents can change their reach and consequences, and not every agent-specific challenge is already settled.
An agent may interpret a request, retrieve information, choose a tool, and trigger an action. If the model is misled or the surrounding software is flawed, the impact depends on the permissions and functions available to it. A text-only assistant and an agent with access to email, cloud files, a command line, or a database are not equivalent security exposures.
What risks do AI agents inherit?
- Software and infrastructure weaknesses: Bugs in the agent’s application, connected services, libraries, hardware, or authentication can expose systems just as they can in other software.
- Identity and access-control failures: A broad or shared credential may let an agent reach data or functions beyond what its task requires.
- Data and model risks: Sensitive data can be exposed or mishandled, while models may be affected by insecure design or data poisoning.
- Availability and integrity risks: A failure or misuse can disrupt a service, alter records, or produce outputs that people or downstream systems rely on.
- Misaligned objectives and specification gaming: An agent can pursue a literal or unintended interpretation of its objective in ways that harm security, even without an attacker supplying malicious input.
These concerns do not all have the same cause. Some are ordinary security defects around the model; others arise from how a model’s output is connected to software functionality. NIST’s January 2026 request for information on securing AI agent systems explicitly asks about security methods, evaluation, gaps in existing approaches, and ways to constrain and monitor agent access. Its comment period closed March 9, 2026; the announcement is a description of the issues under consideration, not a completed agent-security standard. Read NIST CAISI’s announcement.
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How can prompt injection make an AI agent take actions?
Indirect prompt injection occurs when malicious instructions are placed in content an agent may read—such as a document, webpage, or other data source. The agent may treat those instructions as directions and act on them. NIST CAISI calls this agent hijacking and describes possible outcomes such as remote code execution through a command-line-enabled agent, cloud-file exfiltration, or automated phishing. Those are examples of potential impact, not capabilities every agent has: an agent can only take actions its tools and permissions enable.
In its technical blog, published January 17, 2025 and updated December 19, 2025, NIST CAISI reported that in its red-team evaluation, attack success on a held-out set of Workspace tasks ranged from 11% for the strongest baseline attack to 81% for the strongest newly developed attack. In five example injection tasks in its AgentDojo evaluation, average success increased from 57% after one attempt to 80% after 25 attempts per task. These are results from specific evaluation setups, not real-world compromise rates or forecasts for every deployed agent. The task, attack, and consequences matter, and repeated attempts can change measured results because model outputs are probabilistic. NIST CAISI explains the evaluation and its limits.
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What permissions should an AI agent have?
Start with the specific task and grant only the tools, functions, and access it needs. OWASP describes excessive agency as a combination of excessive functionality, excessive permissions, and excessive autonomy. Its guidance emphasizes that these factors interact: a document-reading task is riskier if the available extension can also edit or delete files, or if that extension has broad database access.
- Limit functionality: Enable only necessary extensions and tools. Prefer narrow actions over open-ended capabilities.
- Scope permissions: Give access to the minimum data and operations required for the task, rather than a broad service credential.
- Bind access to the user where practical: Use the user’s own authorization context instead of an all-purpose shared identity, while ensuring the downstream service actually enforces those permissions.
- Require approval for high-impact actions: Put an explicit human check before actions such as sending consequential messages, deleting data, or changing important records.
- Enforce authorization downstream: Do not rely on the model to decide what the user or agent is allowed to do. The connected service must verify authorization independently.
OWASP’s guidance is practical security advice from an industry/open security project, not a binding regulation. Its LLM06:2025 Excessive Agency guidance treats logging, monitoring, and rate limits as ways to limit harm and support response—not substitutes for preventive controls.
How do you secure AI agents?
Constrain what the agent can do
Map the agent’s tools to the task, remove unnecessary functions, and set narrow permissions at the systems those tools call. If an agent only needs to read a subset of documents, its identity should not be able to delete an entire drive. Design high-impact operations so that a model’s decision alone cannot authorize them.
Use approval and containment for consequential actions
Require independent human approval where an action could cause significant or difficult-to-reverse harm. Pair that control with limits on action volume and monitoring of tool calls. Approval should be attached to the actual action and its context, not treated as a general permission for the agent to proceed indefinitely.
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Test for action and consequence, not just a single score
Evaluate realistic tasks and adversarial inputs, including content the agent retrieves from outside its trusted instructions. Repeat tests, adapt them as tools and models change, and track which actions succeeded and what impact they could have. A single aggregate attack-success rate can conceal differences between tasks and consequences; NIST’s AgentDojo discussion shows that repeated attempts can also materially affect measured results.
Treat identity and authorization as ongoing work
NIST’s National Cybersecurity Center of Excellence says traditional identity and access management may not fully address challenges as agents take autonomous actions. Its Agentic AI Identity and Authorization project is developing practical implementation guidance iteratively; the hub reports that a concept paper was published in February 2026 and received more than 600 responses. That work is in progress, not a completed standard. See the NIST NCCoE project hub.
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NIST also says it is developing security control overlays for single-agent and multi-agent use cases, drawing on existing cybersecurity and secure-development resources. This is evidence that established controls are being adapted to agent contexts, not proof that one comprehensive framework already covers every deployment. OWASP’s Agentic AI – Threats and Mitigations is another threat-model-based reference for emerging risks and mitigations; it is guidance, not a regulator’s standard.
