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An AI code-review bot can have a valid identity and still be allowed to do too much. Authentication proves which bot is acting; authorization decides which repository, operation, and resource it may access. Enforce that decision in the system that runs each action—not only in the bot’s prompt.
Is the AI code review bot authenticated but still unauthorized to do this?
Yes. A credential or authenticated session establishes identity; it does not automatically make every action permissible. A bot may be correctly authenticated and still have permissions broader than its review task requires—for example, access to unrelated repositories, the ability to change review state, or permission to invoke tools.
OWASP’s AI Security and Privacy Guide recommends enforcing authorization through backend controls rather than relying on model instructions. As OWASP puts it: “Avoid implementing authorization in Generative AI instructions, as these are vulnerable to hallucinations and manipulation (e.g., prompt injection).” The security decision belongs at an execution boundary such as an API, tool runner, or gateway, where the requested identity, resource, and operation can be checked.
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Pull-request titles, descriptions, comments, diffs, and workflow changes can contain attacker-controlled text. A review agent has to process repository material to do its job, so that content should be treated as untrusted data—not as instructions with authority. If malicious text influences the model and the agent has broadly privileged tools, the result can turn an input-handling problem into an authorization or supply-chain risk.
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OWASP’s AI Security Verification Standard identifies untrusted repository content as a path to AI code-review bots and calls for privileged actions to be adjudicated separately. This describes a risk pattern, not proof that every review bot is vulnerable or that a particular incident occurred. Controls reduce exposure and blast radius; they cannot guarantee that a system will never misuse authority within its permitted scope.
How should authorization be enforced for an AI code review agent?
Make permission decisions outside the model, at the point where an action is executed. The policy check should consider the authenticated identity, the specific resource, the requested operation, and whether that operation is allowed in the current task. A prompt can guide the agent’s behavior, but it is not an access-control mechanism.
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- Default to deny. Grant only the access needed for a defined task, repository, operation, and time window. Use short-lived or revocable credentials where the system supports them.
- Separate read and write authority. A review job that only needs to inspect a change should not receive credentials that can approve, merge, edit repository settings, or write elsewhere.
- Keep untrusted content from becoming an action. Treat PR material as data, isolate it from trusted instructions, and design tool interfaces so text in a diff or comment cannot silently authorize a tool call.
- Isolate the job and limit secrets. Run the agent in a constrained environment; do not expose production credentials, deployment secrets, or unrelated repository access to a routine review.
- Audit decisions and actions. Record enough context to reconstruct which identity acted, on which resource, through which tool, and under what authorization decision.
OWASP’s AI Security Verification Standard, control AC.11.5, says privileged bot actions should use a separate, audited authorization path adjudicated by a policy engine rather than the LLM. That principle applies to approval, merge, labeling, dismissing reviews, comments outside a sandbox, and external tool calls. OWASP’s Secure Coding with AI Cheat Sheet also covers restricted CI-agent permissions, isolation, auditing, and approval gates.
Should an AI code review bot be allowed to merge a pull request?
Not by default. Reviewing code and authorizing its integration are different operations with different consequences. A bot can provide findings or recommendations without receiving merge authority. If an organization chooses to let an agent perform a privileged action, that action should pass an explicit policy check and an appropriate human approval gate, with an audit trail.
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OWASP’s Secure Coding with AI Cheat Sheet addresses approval gates and auditing for AI-assisted development. For security-sensitive changes—especially authentication and authorization logic—AI review should supplement, not replace, accountable human review. OWASP’s Secure Code Review Cheat Sheet discusses centralized access-control decisions and authorization checks after authentication; its DevSecOps Secure Code Review guideline treats AI-assisted review as support for human review.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to check when evaluating an AI review bot’s permissions
Trace an action from the bot’s identity to its effect. The key question is not simply whether the bot is authenticated, but whether every operation is checked against the intended resource and policy at the moment it runs.
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- List the bot’s capabilities. Identify repositories, APIs, tools, secrets, and write operations available to the job—not just the permissions described in its prompt.
- Find the enforcement point. Verify that the API, runner, or gateway checks authorization for each requested operation. A prompt instruction such as “do not merge” is not a substitute for denying merge capability.
- Check scope and duration. Confirm credentials are limited to the task and resource, expire or can be revoked, and do not combine routine read access with unnecessary write privileges.
- Test untrusted-input boundaries. Confirm PR text, diffs, comments, and workflow content are handled as untrusted data and cannot directly grant permission or bypass an action check.
- Inspect privileged-action controls. Verify approvals, merges, workflow changes, and external tool calls require explicit authorization and suitable human review, and that decisions are logged.
- Review security-sensitive changes manually. Check that authentication happens before authorization, and that the authorization decision covers the requested operation and resource rather than merely confirming a valid identity.
OWASP’s AI Agent Security Cheat Sheet covers prompt injection and execution-side authorization checks. Its IDE and AI-Assisted Development guideline discusses sandboxing, scoped tokens, and reviewing coding-agent activity.
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