AI agents create a new attack surface because they can combine a model’s judgment with access to websites, files, memory, tools, and software permissions. An attacker may be able to influence what an agent does through content it reads; a model error can also become consequential when the agent can send a message, retrieve sensitive data, or change a record. The risk depends on the agent’s access and actions—not simply on whether it uses AI.
What makes an AI agent an attack surface?
A conventional chatbot mainly returns text. An agent may also take in outside information, retain or retrieve context, and use tools to act in other systems. That combination gives both attackers and ordinary model errors a path from influence to action.
NIST’s 2026 request for information describes agent security as a mix of familiar software weaknesses and risks that arise when model outputs are combined with software functionality. In practice, the security boundary includes not just the model, but also the data it sees, its identity and permissions, the tools it can call, and the systems those tools reach.
How can an AI agent be hacked?
One important route is indirect prompt injection, which NIST calls agent hijacking. Instead of sending an instruction directly to the model, an attacker puts malicious instructions in content the agent may read, such as a webpage, email, file, or tool result. Because the agent processes both user instructions and external content in the same context, it may follow the embedded instruction instead of treating it solely as untrusted data.
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NIST’s Center for AI Standards and Innovation wrote in a technical blog published January 17, 2025, and updated December 19, 2025: “Currently, many AI agents are vulnerable to agent hijacking, a type of indirect prompt injection in which an attacker inserts malicious instructions into data that may be ingested by an AI agent, causing it to take unintended, harmful actions.” NIST’s evaluation examples included exfiltration and phishing tasks. They demonstrate possible attack paths under test conditions, not that every deployed agent can be compromised in the same way.
Where an attack or failure can travel
| Path | Why it matters |
|---|---|
| External content | A webpage, document, email, or tool output can carry instructions intended to redirect the agent from its task. (NIST CAISI) |
| Overpowered tools | If an agent has write access where read access would suffice, or broad access where a narrow resource is enough, manipulation or an error can have a larger effect. (OWASP) |
| Sensitive data and connected services | Information may be exposed through tool calls, API requests, outputs, or logs. Third-party tools, APIs, and data sources can also create supply-chain risk. (OWASP) |
| Persistent memory and agent workflows | Malicious information may persist in memory or propagate across agents and workflows, creating risks such as memory poisoning and cascading failures. These are risks to assess, not inevitable outcomes. (OWASP) |
| Unbounded activity | Loops or excessive tool use can cause availability problems or unexpected costs, a category OWASP describes as denial of wallet. |
| Model behavior without an attacker | Specification gaming or a misaligned objective can lead an agent to produce harmful results even without malicious input. (NIST 2026 RFI) |
The table describes categories of risk, not proof that each failure is common in deployed systems. An agent’s actual exposure depends on its design and operating environment.
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What do the attack-test results show?
NIST CAISI’s 2025 evaluation illustrates why a single benchmark score cannot stand in for the security of all agents. In the described AgentDojo evaluation, attacks against upgraded Claude 3.5 Sonnet on held-out Workspace tasks succeeded 11% of the time for the strongest baseline attack and 81% for the strongest new attack developed for that model. These are results for that model, task set, and attack design—not real-world compromise rates for AI agents generally.
Attempt count also changed results. Across five selected injection tasks in the same NIST CAISI evaluation, average success was 57% after one attempt and 80% after 25 attempts. A system assessed once may therefore look different from one subjected to repeated attempts. Task choice, attack design, model version, and number of attempts all matter. The reviewed official sources do not establish a broad prevalence statistic for agent compromises in real-world deployments.
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How do you secure an AI agent?
Start by limiting what a successful manipulation or mistaken action could do. NIST highlights agent identification and authorization; OWASP recommends least privilege and limiting an agent to the tools it needs. Access controls reduce potential impact, but they do not guarantee that an agent will resist manipulation.
For developers and organizations
- Inventory the agent’s access. Record its tools, data sources, identity, credentials, and possible actions. Include connected services and the content that enters its context.
- Grant only task-required permissions. Separate read and write access, scope permissions to specific resources, and avoid giving an agent broad or persistent credentials when narrower access will do.
- Require authorization for consequential actions. Set explicit approval requirements for sensitive operations, such as sending external communications or changing important records.
- Monitor tool use and keep audit records. Track tool calls and relevant authorization decisions so unusual activity can be investigated. NIST’s identity work also raises auditing and non-repudiation as considerations.
- Test the deployed setup under realistic conditions. Include indirect prompt injection, sensitive-data access, high-impact operations, and repeated attempts. Test with the actual tools and task context, and examine task-specific outcomes rather than relying only on an aggregate score.
For people using an agent
- Limit access to sensitive data and credentials; use a logged-out mode when the task does not require an account.
- Review consequential actions before approving them, and watch the agent when it is operating on sensitive sites.
- Give narrow, explicit instructions that define the task and relevant boundaries.
These practices are risk-reduction measures, not guarantees. OpenAI recommends them for agent use; the appropriate controls depend on the product and the task.
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What should you compare when choosing an agent?
There is no single security score that captures every deployment. Compare agents or designs using the same task and threat assumptions, and check the details that determine what an agent can see and do.
- Permission scope: read versus write access, which resources are in scope, and whether credentials persist beyond a task.
- Action consequences: whether the agent can send communications, make purchases, modify records, or take irreversible actions—and whether approval is required.
- Untrusted content exposure: which websites, emails, documents, tools, and retrieval sources can enter the agent’s context.
- Evaluation quality: which attacks and tasks were tested, the model and version, the number of attempts, and how closely testing matches the deployment.
- Monitoring and accountability: whether tool calls, identity, authorization decisions, and audit records are visible.
These are comparison criteria drawn from NIST and OWASP risk and control themes, not a ranking of vendors.
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What is NIST doing about agent security?
NIST CAISI announced a request for information on secure agent development and deployment on January 12, 2026, seeking input on threats, measurement, and ways to constrain and monitor access. On February 5, 2026, NIST’s National Cybersecurity Center of Excellence announced an agent identity and authorization concept paper; its public comment period ended April 2, 2026. NIST’s security overview describes planned control overlays for both single-agent and multi-agent systems. This is ongoing standards and guidance work, not a completed universal compliance standard.
In its February 5, 2026 announcement, NIST NCCoE said: “However, realizing these benefits requires understanding the potential risks from giving AI agents access to diverse data sets, tools, and applications, and applying appropriate identification and authorization controls to mitigate these risks.” That emphasis reflects the central practical issue: an agent’s identity and permissions must be designed and governed alongside its model behavior.
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