CVE-2018-1000538 in S3 Server
Summary
by MITRE
Minio Inc. Minio S3 server version prior to RELEASE.2018-05-16T23-35-33Z contains a Allocation of Memory Without Limits or Throttling (similar to CWE-774) vulnerability in write-to-RAM that can result in Denial of Service. This attack appear to be exploitable via Sending V4-(pre)signed requests with large bodies . This vulnerability appears to have been fixed in after commit 9c8b7306f55f2c8c0a5c7cea9a8db9d34be8faa7.
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Analysis
by VulDB Data Team • 03/29/2023
The vulnerability identified as CVE-2018-1000538 affects Minio Inc.'s S3 server implementation prior to the RELEASE.2018-05-16T23-35-33Z version, representing a critical memory allocation flaw that falls under the CWE-774 category of allocation of memory without limits or throttling. This weakness specifically manifests in the write-to-RAM functionality of the server, creating a pathway for adversaries to exploit memory consumption patterns that can lead to system-wide denial of service conditions. The vulnerability is particularly concerning because it operates at the core memory management layer of the S3-compatible storage system, where improper resource handling can cascade into complete service unavailability.
The attack vector for this vulnerability involves sending V4-pre-signed requests containing large request bodies, which triggers the unbounded memory allocation behavior within the Minio server's processing pipeline. When these requests are processed, the server allocates memory buffers without proper size limitations or throttling mechanisms, allowing an attacker to consume excessive RAM resources through carefully crafted requests. This memory exhaustion occurs during the write operations to RAM, where the server's memory management system fails to implement adequate checks on incoming payload sizes, particularly when dealing with signed requests that are designed to be processed with elevated privileges.
The operational impact of this vulnerability extends beyond simple resource exhaustion, as it can effectively disable the entire Minio S3 server instance by consuming all available memory resources. This creates a denial of service condition that affects legitimate users and can be exploited by malicious actors to disrupt storage services, potentially causing data unavailability for critical applications that depend on the Minio server. The vulnerability's exploitation is particularly dangerous in cloud environments where Minio servers may be handling multiple concurrent requests from various clients, as a single malicious request can trigger cascading memory exhaustion across the entire system.
Security mitigations for this vulnerability involve upgrading to the fixed version released after commit 9c8b7306f55f2c8c0a5c7cea9a8db9d34be8faa7, which implements proper memory allocation limits and throttling mechanisms. Organizations should also implement request size validation at network boundaries, employ rate limiting controls, and monitor memory consumption patterns to detect potential exploitation attempts. The fix addresses the root cause by introducing bounded memory allocation practices that prevent uncontrolled growth of memory buffers during request processing. Additionally, implementing proper input validation and request sanitization measures at multiple layers of the system can provide defense-in-depth protection against similar memory management vulnerabilities that may exist in other components of the storage infrastructure.
This vulnerability aligns with ATT&CK technique T1499.004 for network denial of service and demonstrates the importance of proper resource management in cloud storage systems. The memory allocation flaw represents a classic example of how insufficient input validation and resource bounds checking can create exploitable conditions in distributed storage systems, particularly those implementing S3-compatible APIs that handle large data transfers. The fix implemented by Minio Inc. addresses the underlying memory management issues by introducing proper allocation limits that prevent the server from consuming excessive resources during request processing. Organizations should also consider implementing monitoring solutions that can detect anomalous memory consumption patterns and automatically trigger alerts when memory usage exceeds predefined thresholds, providing early warning capabilities against potential exploitation attempts.