Give an AI agent only the tools and permissions its task needs, and never let the model’s own decision authorize a consequential action. Agent security depends on the application around the model: the data it can consume, the actions it can request, the checks that govern execution, and the evidence developers collect by testing and logging those actions.
Why tool access changes the security problem
An agent can do more than generate text: it may plan tasks, retain memory, use tools, and act on external systems. That turns model errors and hostile instructions into possible side effects—such as exposing data, changing files, sending messages, or triggering a workflow. OWASP’s AI Agent Security Cheat Sheet covers risks including prompt injection, tool abuse, privilege escalation, data exfiltration, memory poisoning, goal hijacking, approval manipulation, cascading failures, and supply-chain attacks.
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Start with the application’s actual authority, not a label such as “AI assistant” or “security operator.” Record what the agent can read and change, which network destinations it can reach, what credentials it can use, and which operations are irreversible or visible outside the system. Read-only search and database deletion do not present the same impact, even if both are exposed through a tool call.
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Map the agent’s reach
- Data: Which files, messages, records, and memory stores can it read or modify?
- Tools: Can it search, write, execute code, send communications, install packages, or administer services?
- Credentials: Which tokens or secrets are available to the process, and what scope and lifetime do they have?
- Network: Which destinations can the agent contact, and can it send data to arbitrary endpoints?
- Consequences: Which actions affect customers, production systems, money, or data that cannot readily be restored?
How an agent can be hijacked through data
Prompt injection does not have to arrive in the user’s message. NIST describes agent hijacking through malicious instructions embedded in material an agent consumes, including email, files, and websites. Content that appears relevant to the task can attempt to redirect the agent or induce an unintended action. See the NIST CAISI evaluation discussion.
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In development workflows, treat issue descriptions, pull-request comments, README files, dependency changelogs, error traces, fetched pages, and MCP responses as untrusted input. A system prompt that tells a model to ignore hostile instructions, or delimiters around retrieved content, can help guide behavior but should not be treated as an authorization boundary. Use scoped tools and validate actions independently; OWASP’s agent security guidance recommends treating external data as untrusted.
Reduce capability before relying on warnings
OWASP identifies excessive functionality, excessive permissions, and excessive autonomy as common causes of excessive agency. Its practical implication is to remove capabilities the task does not need, narrow the remaining access, and limit how freely the agent can act. For example, an agent asked to summarize a mailbox can use read-only access; it need not receive permission to send mail. A person can review and send a proposed reply separately. See OWASP’s Excessive Agency guidance.
Choose the narrower implementation
| Design area | Lower-risk option | Higher-risk option |
|---|---|---|
| Tool capability | Narrow, task-specific functions | Broad shell, network, administrator, or write access |
| Permission scope | Resource-specific and read-only where possible | Long-lived, organization-wide, write-capable credentials |
| Execution authority | Independent policy check after the agent proposes an action | Model output directly triggers an action |
| Autonomy | Approval for high-impact or irreversible operations | Unreviewed execution of external or destructive actions |
| Isolation | Ephemeral sandbox with limited credentials and network egress | Shared developer machine or CI environment with broad secrets |
| Evaluation | Adaptive, task-specific testing with repeated attempts | One static benchmark or a single pass/fail run |
| Auditability | Structured records of tool calls, authorization, approval, and outcome | Missing or untrusted logs |
These are design comparisons synthesized from OWASP’s agent controls, its Excessive Agency guidance, and NIST’s evaluation lessons; they do not rank commercial products.
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Keep authorization outside the model
The agent can propose an action, but a separate policy or execution component should decide whether it is allowed. That component can check the action against the authenticated user, the target resource, the tool’s permitted operations, and any required approval. OWASP states the principle succinctly: “Separate decision-making from execution.” See the AI Agent Security Cheat Sheet.
Make an approval specific and verifiable
Approval is meaningful only when it is tied to the action that will actually run. Show the reviewer the tool, destination, affected resource, and normalized parameters. Validate the approval outside the model, bind it to the intended actor and action, and expire it after a defined interval. For sensitive operations, short-lived authorization and replay protection help prevent an old approval from being reused for a changed action.
Use a risk-based policy rather than treating every tool call alike. OWASP gives illustrative examples: reading and searching are low risk; writing is medium; sending email or executing code is high; deleting a database or transferring funds is critical. Those categories are examples, not universal policy values. For high-impact or irreversible actions, provide a preview, require explicit approval, keep an audit trail, and provide a way to interrupt or roll back where possible. Fail closed if risk classification, policy lookup, approval validation, or audit logging fails. These controls are described in the OWASP guidance.
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Protect coding agents and the route to production
Coding agents can cross several trust boundaries in one task. OWASP’s Secure Coding with AI Cheat Sheet describes tools that may execute shell commands, install packages, edit files, run tests, access networks, or push branches. Relevant boundaries include developer permissions, repository content, model-provider calls, MCP servers, CI/CD workflows, organizational secrets, and deployment access.
Bound the development environment
- Run agents in a sandbox. Restrict commands, writable file paths, credential access, and network egress to what the task requires.
- Audit and allowlist MCP servers and tools. Pin tool definitions and review their descriptions; descriptions can contain instructions and may change after approval.
- Verify AI-suggested packages on their public registry, and check dependency versions for known vulnerabilities before merging.
- Review every changed file rather than relying on the agent’s summary. Apply particular scrutiny to rules files, CI/CD workflows, Dockerfiles, build scripts, deployment configuration, and package scripts.
- Treat issue and pull-request content as attacker-controlled when it is passed to CI agents. Isolate jobs and limit their credentials.
- Inspect agent-authored test changes for removed tests, weakened assertions, or mocks that eliminate the behavior under test. A green test suite produced by the same agent is not independent assurance.
These controls are drawn from OWASP’s secure-coding guidance. The same execution-boundary principle applies beyond coding: inspection and authorization should not depend solely on the agent whose behavior is being controlled.
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Test the attack surface you actually expose
Evaluation should reproduce the tools, data sources, permissions, and consequences of the intended deployment. NIST CAISI used the AgentDojo framework, with simulated Workspace, Travel, Slack, and Banking environments, and added scenarios for remote code execution, database exfiltration, and automated phishing. Its findings apply to the reported benchmark and agent setup, not to every deployed agent.
What the reported figures mean
| Figure | Scope |
|---|---|
| 11% to 81% | In NIST CAISI’s 2025 Workspace red-team evaluation of upgraded Claude 3.5 Sonnet, the strongest new attack designed for that model succeeded 81% of the time, compared with 11% for the strongest baseline attack. |
| 57% | Average success across five reported injection tasks on one attempt. |
| 80% after 25 attempts per attack | Average success across those same five tasks after each attack was attempted 25 times. |
These are attack success rates in NIST CAISI’s reported evaluation, published January 17, 2025 and updated December 19, 2025—not production incident probabilities, forecasts, or guarantees of current model performance. Repeated attempts matter when an attacker can retry cheaply. The source also emphasizes updating evaluations as attacks adapt, measuring task-specific outcomes as well as aggregates, and accounting for impact as well as success rate. A low success rate in a high-impact code-execution or exfiltration scenario does not make the scenario harmless. Read the NIST CAISI post for the reported setup and findings.
Build evaluation around consequences
- Test indirect instructions embedded in the actual classes of content the agent will read.
- Measure whether the agent completes the intended task and whether it attempts unauthorized actions, leaks data, or changes protected state.
- Run multiple attempts against each attack scenario when retries are plausible; record both success rate and severity of the resulting action.
- Repeat evaluations when models, tools, prompts, permissions, or external data sources change.
- Verify that policy checks, approvals, logging, and rollback work when the model is manipulated or an infrastructure dependency fails.
OWASP’s MCP Top 10 is a beta, living project page, so treat it as evolving guidance rather than a fixed standard. OWASP also publishes an agentic AI threats and mitigations resource for further threat-model coverage.
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