Giving an AI agent access to security tools lets it act on connected systems, not just produce text. If it is misled by malicious content, makes a mistake, or interprets its task in an unintended way, its tool access can turn that behavior into unauthorized changes, data exposure, deletion, or other harm. The level of risk depends on what the tools can do, which identities and resources they can reach, and whether consequential actions require independent authorization.
Why tool access changes the risk
An agent’s impact is bounded by the functions it can call and the authority those functions carry. A read-only tool limited to one dataset has a different potential impact from an open-ended command interface running under a broadly privileged identity. OWASP notes that excessive agency can affect confidentiality, integrity, and availability, depending on the systems an application can reach.
Risk is not limited to malicious users. NIST identifies attacks through adversarial data and insecure or poisoned models, as well as harmful actions that can arise without an attacker—for example, when an agent games a specification or pursues a misaligned objective. No single control eliminates every risk.
How an agent can cause harm
Indirect prompt injection and hijacking
An agent can encounter hostile instructions in material it is asked to process, not only in a direct user prompt. NIST describes indirect prompt injection in emails, files, and websites. If the agent treats those instructions as authoritative and has powerful tools, it may be diverted from the user’s task.
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NIST CAISI’s technical staff describe the underlying problem as a failure to separate trusted instructions from untrusted data: “AI agent hijacking is the latest incarnation of an age-old computer security problem that arises when a system lacks a clear separation between trusted internal instructions and untrusted external data — and is therefore vulnerable to attacks in which hackers provide data that contains malicious instructions designed to trick the system.”
In evaluation scenarios, CAISI considered objectives such as using command-line access to download and run a program from an untrusted URL, exfiltrating cloud files, and sending phishing emails. These are tested attack objectives, not evidence of how often such attacks succeed in production or an industry-wide prevalence estimate. NIST CAISI’s evaluation discussion also emphasizes that tests should evolve as systems change and be tailored to the tasks an agent performs.
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Too many functions or excessive permissions
A tool may expose operations beyond what a task requires. OWASP gives the example of an agent asked to read documents whose plugin also allows them to be modified or deleted. Open-ended shell or command tools can create an especially broad action space.
Even a narrowly described tool can carry excessive authority through its underlying identity. OWASP describes a read-oriented database integration whose credentials also allow updating, inserting, and deleting records, as well as a user-facing integration that instead uses a generic privileged identity able to access other users’ files. In either case, tool descriptions alone do not enforce the intended boundary; permissions must be limited in the downstream system.
Unreviewed actions
When an agent can complete high-impact operations without separate validation or approval, a mistaken or manipulated output can become a real change. OWASP uses deletion without user confirmation as an example and recommends human review for actions such as sending a message or publishing a post. The same principle applies to security operations that alter systems, affect accounts, or expose sensitive information.
Data, credentials, and tool-chain exposure
Tool calls, APIs, returned data, credentials, and protocol logs can all become exposure paths. OWASP’s living MCP Top 10 identifies risks including token mismanagement and secret exposure, tool poisoning, software supply-chain attacks, command injection, inadequate authentication and authorization, and missing audit telemetry. Tool definitions and dependencies therefore belong inside the security boundary, not outside it.
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What determines the risk of a particular setup?
Assess the configuration across these dimensions rather than treating all agent integrations as equally risky.
| Dimension | Lower exposure | Higher exposure |
|---|---|---|
| Permission level | Read-only access or narrowly constrained writes | Unrestricted write access |
| Environment | Trusted, bounded resources | Untrusted resources, such as the open internet |
| Identity and resource scope | A task-specific identity limited to the relevant user, account, repository, host, or dataset | A generic privileged identity with access to unrelated users or systems |
| Function scope | Specific, typed operations | Open-ended shell, code execution, or command functions |
| Action impact and reversibility | Reading information or making a reversible, constrained change | Deleting, publishing, or making a consequential change that is difficult to reverse |
| Oversight and observability | Independent approval for high-impact operations and auditable tool calls | Unreviewed actions with little record of calls or context changes |
NIST’s 2025 tool-use taxonomy distinguishes read-only, constrained-write, and write access, and describes examples such as deep research, browser use, and computer use in untrusted environments. These categories help frame a review; they are not a complete risk rating for a specific deployment. See NIST’s tool-use lessons.
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How to limit the authority you grant
- Start with the task, then grant only what it needs. Remove unused functions and favor specific operations over open-ended commands. OWASP’s AI Agent Security Cheat Sheet recommends minimizing tools and permissions.
- Separate reading from writing. Begin with read-only access where possible. Add only the narrowly scoped write operations needed for the workflow rather than granting unrestricted write capability.
- Use a scoped identity. Bind actions to the relevant user or task and restrict downstream access to the resources and operations required. Avoid generic, high-privilege credentials. NIST’s identity and authority concept paper highlights identity, authorization, auditing, and non-repudiation as areas of focus for software agents.
- Enforce authorization at the action boundary. The downstream service or tool should validate every request against policy. Do not make the model the sole judge of whether an operation is authorized.
- Put human approval where impact is high. Require a person to approve actions such as deletion, publication, or other consequential changes. Where possible, enforce approval at the tool or downstream API boundary so it cannot be bypassed by a model decision.
- Constrain and monitor runtime access. Limit which tools and resources are available during execution, and monitor their use. NIST CAISI identifies interventions that limit and monitor agent access as an area for security work in its request for information on securing AI agent systems.
- Keep an audit trail. Record the agent identity, tool calls, authorization decisions, and relevant context changes. Useful telemetry supports investigation and accountability; missing telemetry can make incidents harder to understand.
- Test the actual workflow and update the tests. Evaluate how the agent behaves with task-relevant untrusted inputs and the tools it will use. CAISI notes that task-specific performance and multiple attempts can help assess hijacking risk; a result from one evaluation is not a universal safety guarantee.
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