CVE-2018-1281 in MXNet
Summary
by MITRE
The clustered setup of Apache MXNet allows users to specify which IP address and port the scheduler will listen on via the DMLC_PS_ROOT_URI and DMLC_PS_ROOT_PORT env variables. In versions older than 1.0.0, however, the MXNet framework will listen on 0.0.0.0 rather than user specified DMLC_PS_ROOT_URI once a scheduler node is initialized. This exposes the instance running MXNet to any attackers reachable via the interface they didn't expect to be listening on. For example: If a user wants to run a clustered setup locally, they may specify to run on 127.0.0.1. But since MXNet will listen on 0.0.0.0, it makes the port accessible on all network interfaces.
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Analysis
by VulDB Data Team • 03/22/2023
The vulnerability described in CVE-2018-1281 affects Apache MXNet versions prior to 1.0.0 and represents a critical network exposure issue within the framework's distributed computing capabilities. This flaw exists in the clustered setup configuration where the scheduler component is designed to accept user-specified IP addresses and ports through environment variables DMLC_PS_ROOT_URI and DMLC_PS_ROOT_PORT. The vulnerability stems from a misconfiguration logic error where the system ignores user-defined network binding parameters and defaults to listening on all available network interfaces. This behavior fundamentally undermines the security assumptions of network isolation that users expect when configuring local development environments or controlled network setups.
The technical implementation flaw manifests in how MXNet handles network interface binding during scheduler initialization. When users specify a particular IP address such as 127.0.0.1 for local testing purposes, the framework fails to respect this configuration and instead binds to 0.0.0.0, which represents all network interfaces on the host system. This creates an unintended attack surface where any network interface that has the MXNet scheduler port open becomes accessible to external network entities. The vulnerability is particularly concerning because it operates at the network protocol level, bypassing typical application-level security controls and directly exposing system services to potential exploitation. According to CWE-692, this represents a weakness where a security control is bypassed, and it aligns with ATT&CK technique T1071.004 for application layer protocol usage, specifically involving the binding of network services to unintended interfaces.
The operational impact of this vulnerability extends beyond simple network exposure to encompass broader security implications for machine learning infrastructure deployments. Attackers can leverage this flaw to gain unauthorized access to distributed computing resources, potentially leading to data exfiltration, model manipulation, or resource exhaustion attacks. In production environments, this vulnerability could enable lateral movement attacks where compromised systems serve as entry points for accessing other network segments. The risk is particularly elevated in containerized environments or cloud deployments where multiple services may share network interfaces, as the scheduler port becomes accessible to any entity that can reach the host system. Organizations using MXNet for distributed training or inference workloads face significant risk when running affected versions, as the vulnerability can be exploited without requiring authentication or specialized attack vectors. The flaw also impacts the principle of least privilege, as it allows network services to operate with broader access permissions than explicitly configured by administrators.
Mitigation strategies for CVE-2018-1281 require immediate version upgrades to Apache MXNet 1.0.0 or later, which contain the fixed implementation that properly respects user-defined network binding parameters. System administrators should conduct comprehensive inventory assessments to identify all affected systems and ensure that network firewalls are properly configured to restrict access to MXNet scheduler ports, particularly in environments where the vulnerability cannot be immediately patched. Network segmentation practices should be implemented to isolate ML compute environments from production networks, and regular security scanning should be performed to detect any instances running vulnerable versions. The fix addresses the core issue by ensuring that the DMLC_PS_ROOT_URI environment variable is properly respected during scheduler initialization, thereby preventing the unintended binding to 0.0.0.0. Organizations should also implement monitoring for unusual network access patterns to the MXNet scheduler ports, as this can serve as an early detection mechanism for potential exploitation attempts. Additionally, security teams should review their incident response procedures to ensure preparedness for potential exploitation of this vulnerability, considering that it may be used in combination with other attack vectors targeting machine learning infrastructure.