CVE-2017-5719 in Deep Learning Training Tool
Summary
by MITRE
A vulnerability in the Intel Deep Learning Training Tool Beta 1 allows a network attacker to remotely execute code as a local user.
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Analysis
by VulDB Data Team • 12/09/2019
The vulnerability identified as CVE-2017-5719 resides within Intel's Deep Learning Training Tool Beta 1, a software framework designed to facilitate machine learning model training processes. This tool represents a critical security weakness that bridges the gap between network-based attacks and local privilege execution, creating a severe risk landscape for organizations utilizing Intel's AI development platforms. The vulnerability stems from insufficient input validation mechanisms within the tool's network communication protocols, specifically affecting how the application handles remote data processing requests. Security researchers have classified this issue as a remote code execution flaw that can be exploited by adversaries positioned outside the local network perimeter, making it particularly dangerous for enterprise environments where such tools may be exposed to external threats.
The technical implementation of this vulnerability involves a buffer overflow condition that occurs when the Deep Learning Training Tool processes specially crafted network requests containing malformed data structures. This flaw operates at the application layer and leverages the tool's inherent functionality to accept and process external inputs without proper sanitization checks. The vulnerability is categorized under CWE-121, which details stack-based buffer overflow conditions, and aligns with ATT&CK technique T1203, which covers legitimate credentials and privilege escalation through remote access tools. When exploited, the vulnerability allows an attacker to inject malicious code that executes with the privileges of the local user account running the training tool, effectively providing a foothold for further system compromise.
The operational impact of CVE-2017-5719 extends beyond immediate code execution capabilities, creating a comprehensive attack surface that can be leveraged for persistent system infiltration. Organizations deploying the Deep Learning Training Tool in production environments face significant risks including unauthorized access to sensitive training data, potential model theft, and establishment of backdoor access points for continued exploitation. The vulnerability's remote exploitability means that attackers can target systems without requiring physical access or prior authentication, making it particularly attractive for automated attack campaigns. Network security teams must consider this vulnerability as a potential entry point for advanced persistent threats, especially in environments where machine learning workloads are processed using Intel's proprietary frameworks. The attack vector typically involves sending crafted network packets to the tool's listening ports, triggering the buffer overflow condition that subsequently allows code injection.
Mitigation strategies for CVE-2017-5719 require immediate implementation of network segmentation measures to isolate the Deep Learning Training Tool from external network access, particularly through the use of firewalls and access control lists. Organizations should implement the principle of least privilege by running the tool with minimal required permissions and consider deploying network monitoring solutions to detect anomalous traffic patterns associated with exploitation attempts. The vulnerability's nature suggests that patching the software component is essential, though organizations must verify that updates do not disrupt existing machine learning workflows or training processes. Security controls should include regular vulnerability scanning of network infrastructure to identify any instances of the vulnerable tool, along with implementing intrusion detection systems capable of identifying the specific attack signatures associated with this vulnerability. Additionally, organizations should consider adopting containerization or virtualization strategies to limit the blast radius of potential exploitation, ensuring that even if the vulnerability is successfully exploited, the attacker's access remains restricted to the isolated environment.