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Introduction:
A critical security vulnerability has been discovered within the architectural fabric of S3-compatible object storage services, a cornerstone of modern cloud and on-premises infrastructure. This flaw, identified during a Synack Red Team engagement, threatens platforms like MinIO and Supabase, revealing how a misconfiguration in one layer can cascade into a systemic data breach. As organizations increasingly rely on these interoperable storage solutions, understanding and hardening their configuration is paramount to preventing unauthorized data access and exfiltration.
Learning Objectives:
- Understand the common misconfiguration vectors in S3-compatible object storage backends.
- Learn practical methods to test for insecure permissions and bucket policies.
- Implement hardening measures for MinIO and Supabase deployments to mitigate exploitation risk.
You Should Know:
1. Mapping the S3-Compatible Attack Surface
The vulnerability stems from the widespread adoption of the Amazon S3 API standard by countless third-party services. While promoting interoperability, this also standardizes attack vectors. The core issue often lies in overly permissive bucket policies, Access Control Lists (ACLs), or service account credentials that are inadvertently applied.
Step-by-step guide:
First, identify your S3-compatible endpoints. These could be MinIO (minio.example.com), Supabase Storage (project-ref.supabase.co/storage/v1), or other custom endpoints.
Using the AWS CLI configured for a target endpoint aws --endpoint-url=https://minio.target.com s3 ls Using curl for a basic probe curl -v https://storage.target.com/
This initial reconnaissance confirms service availability. The next step is to enumerate accessible buckets if credentials (even default ones) are known or if the service allows anonymous listing.
aws --endpoint-url=https://minio.target.com s3 ls s3:// If anonymous access is enabled, this may list all buckets.
2. Testing for Insecure Bucket Policies & ACLs
The primary exploitation path is through misconfigured permissions. A common pitfall is applying a policy like `”Effect”: “Allow”, “Principal”: “”, “Action”: “s3:”` to sensitive data buckets.
Step-by-step guide:
Use the AWS CLI or `mc` (MinIO Client) to audit policies.
List bucket policies aws --endpoint-url=https://minio.target.com s3api get-bucket-policy --bucket sensitive-data Check bucket ACL aws --endpoint-url=https://minio.target.com s3api get-bucket-acl --bucket sensitive-data
Analyze the output. Look for `”Principal”: “”` which signifies public access. Also, check for conditions that are too broad or missing. For a simulated exploit, if a bucket is writable by the public, an attacker can upload a malicious file:
echo "malicious payload" > exploit.html aws --endpoint-url=https://minio.target.com s3 cp exploit.html s3://sensitive-data/
3. Exploiting Credential Leakage in Application Layers
Services like Supabase front their S3-compatible storage with a management API. Vulnerabilities in the application layer (e.g., insecure direct object references, IDOR) can lead to the exposure of underlying pre-signed URLs or service role keys with excessive privileges.
Step-by-step guide:
Intercept traffic from a client application (e.g., a web app using Supabase Storage). A pre-signed URL might be fetched:
GET /storage/v1/object/private/bucket/secretfile.pdf?downloadToken=eyJhbGciOiJ...
This token, if not properly scoped to a user/session, can be replayed by an attacker. Test this by copying the full URL and accessing it in an incognito browser session. Furthermore, if a service key is leaked, it can be used directly:
Setting a leaked Supabase service key for API access curl 'https://project-ref.supabase.co/storage/v1/bucket' \ -H "Authorization: Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..."
4. Server-Side Request Forgery (SSRF) to Internal Metadata
In cloud environments, the S3-compatible service might have permissions to access cloud metadata services (like AWS IMDS). If the storage service itself is vulnerable to SSRF (e.g., via a maliciously crafted image processing request), it can become a pivot point to steal cloud credentials.
Step-by-step guide:
This requires a functional SSRF vector. For example, if a service fetches images from a URL, try to make it fetch the internal metadata endpoint.
Attempting to access AWS IMDSv2 from a compromised service context curl -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 21600" curl -H "X-aws-ec2-metadata-token: $TOKEN" http://169.254.169.254/latest/meta-data/iam/security-credentials/
Mitigation involves hardening the service’s network permissions to block access to `169.254.169.254` and other internal endpoints.
5. Hardening Your S3-Compatible Deployment
Proactive defense is critical. Apply the principle of least privilege at every layer.
Step-by-step guide:
For MinIO: Use `mc` to set restrictive policies.
Create a policy that denies public access mc admin policy create myminio deny-public ./deny-public-policy.json Attach it to all service accounts
Policy JSON (`deny-public-policy.json`):
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Deny",
"Principal": "",
"Action": "s3:",
"Resource": "arn:aws:s3:::",
"Condition": {"StringEquals": {"aws:PrincipalType": "Anonymous"}}
}
]
}
For Supabase: Enable Row Level Security (RLS) on your Storage tables in the Supabase dashboard and write strict policies. Never use the service `anon` key with write permissions. Regularly rotate the `service_role` key.
Network Security: Place storage backends in private subnets. Use VPC Endpoints or allow-lists for ingress traffic, and disable all public access blocks unless absolutely necessary.
What Undercode Say:
- The Shared Responsibility Model is a Chain: Using a managed S3-compatible service (like Supabase Storage) does not absolve you of security responsibility. You are responsible for configuring bucket policies, ACLs, and application-layer access controls. The platform provider secures the infrastructure, but you must secure your data within it.
- Standardization Amplifies Risk: The power of the S3 API standard is also its greatest weakness. An attacker’s toolkit and knowledge, built for Amazon S3, are directly transferable to a vast array of other services, making automated scanning and exploitation highly efficient.
This vulnerability is a stark reminder that “compatible” often means “attack surface is identical.” The flaw is less a novel zero-day and more a systemic failure in applying security fundamentals to a new generation of cloud-native infrastructure. As AI pipelines and serverless backends increasingly depend on object storage for model weights, training data, and user content, the impact of such misconfigurations will only grow. Future attack chains will likely automate the discovery of these exposed storage endpoints from public code repositories and leverage stolen data to poison AI models or facilitate large-scale ransomware campaigns. The time for assumption-based security is over; continuous configuration auditing and adherence to least-privilege are non-negotiable.
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Reported By: Gusti Agung – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
