A Lambda function’s access to Amazon S3 comes from its IAM execution role. If that role allows broad S3 actions on broad resources, the function may be able to do far more than its code needs. That does not, by itself, make an S3 bucket public; role permissions and bucket-level access are separate controls.
To reduce risk, find the function’s execution role, compare its policies with the S3 operations and resources the workload actually needs, and narrow the permissions. Then check bucket policies and other S3 controls separately if your concern is public or cross-account access.
Why does my Lambda have admin access to S3?
Lambda runs your function using an execution role. AWS grants the function temporary credentials for that role, and the role’s permissions determine which AWS resources the function can access. The function itself does not have a separate S3 identity.
If the role’s identity policies grant wide-ranging S3 actions against many buckets or objects, the function can inherit that reach—even if its current code uses only a small part of it. The actual access depends on the policies and applicable controls; the title does not establish that any particular account, function, or role has been inspected.
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A broad role policy also does not automatically make a bucket public. A role policy governs what an identity such as the Lambda function can do. Public or shared access is a separate question involving bucket policies, ACLs, and access-point policies. AWS explains execution-role permissions in its Lambda execution role guidance.
How do I limit an AWS Lambda function to one S3 bucket?
- Find the role. In the AWS Lambda console, open Functions, choose the function, and open Configuration > Permissions. Follow the execution-role link to IAM.
- Review the role’s policies. Check both policies attached directly to the role and any relevant permissions boundaries or other controls. Look for S3 actions granted broadly, especially policies whose resource scope includes a wildcard rather than the specific bucket or object locations the function needs.
- Map the workload. Identify the S3 operations the code performs—such as reading, writing, listing, or deleting—and which bucket and object paths those operations require. Include scheduled and infrequent flows, not just the function’s most common run.
- Narrow actions and resources. Replace unnecessary broad S3 permissions with the smallest set of actions and bucket or object resources that support the workload. Keep any required CloudWatch logging permissions; AWS’s basic Lambda execution-role guidance includes permissions for writing logs.
- Review the result. Use IAM policy validation and Access Analyzer findings as aids, then exercise the function’s expected flows and check for failures. AWS’s policy validation guidance cautions that wildcards and overly broad permissions can expand access beyond intent.
There is no universal S3 policy snippet for every Lambda: the right actions and resources depend on what the code does. A function that only reads objects should not retain write or delete permissions merely because they were convenient during development. Conversely, narrowing permissions without accounting for an occasional workflow can break a legitimate job.
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How can I tell which S3 permissions the function actually needs?
Start with the code and workload requirements, then use observed activity as supporting evidence. IAM Access Analyzer can use CloudTrail activity over a selected date range to generate a policy template based on permissions used. AWS recommends it as a way to help identify required execution-role permissions in its Lambda execution role documentation; the generated template should be reviewed, not treated as a complete guarantee.
Observed activity can miss rarely used paths. In its role-permission recommendations, AWS uses the last 30 days of activity. A permission needed only for a quarterly job could therefore appear unused. Check schedules, recovery procedures, and business requirements against the observation window before removing a permission. See AWS’s IAM Access Analyzer guidance on unused access.
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Consider the difference between evidence sources when refining the policy:
- Code and workload requirements identify intended operations and resources, including flows that may not have run recently.
- Observed CloudTrail activity can show permissions used during the selected period, but does not prove that unobserved permissions are unnecessary.
How do I check whether an S3 bucket is public or shared?
Inspect bucket-level access controls separately from the Lambda role. AWS IAM Access Analyzer for S3 can review bucket policies, ACLs, and access-point policies for public or shared access. This helps answer whether a bucket is exposed beyond the intended accounts or users; it does not replace reviewing what the Lambda execution role itself is allowed to do. See AWS’s S3 Access Analyzer documentation.
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If an analyzer finding shows unintended access, identify which policy or control permits it, change that policy, and rescan to confirm the finding is addressed. AWS describes findings as a guide to remediation in its IAM Access Analyzer overview.
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What should I verify after changing the role policy?
- The function completes its intended reads, writes, and other required S3 operations.
- Scheduled or infrequent jobs still work, including tasks outside a recent activity window.
- Unneeded broad actions and resource scopes have been removed without removing essential logging permissions.
- Relevant Access Analyzer findings have been reviewed, and unintended access has been corrected at the policy responsible.
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