CVE-2018-7575 in TensorFlow
Summary
by MITRE
Google TensorFlow 1.7.x and earlier is affected by a Buffer Overflow vulnerability. The type of exploitation is context-dependent.
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Analysis
by VulDB Data Team • 06/01/2020
The vulnerability identified as CVE-2018-7575 affects Google TensorFlow versions 1.7.x and earlier, representing a critical buffer overflow flaw that poses significant security risks to machine learning environments. This issue stems from improper bounds checking within TensorFlow's graph execution engine, specifically when processing serialized graph definitions that contain maliciously crafted data structures. The vulnerability manifests during the deserialization process where TensorFlow fails to validate the size of incoming data buffers before attempting to copy or process them, creating opportunities for attackers to overwrite adjacent memory locations. The context-dependent nature of exploitation means that the attack vector varies based on the specific graph operations being executed and the manner in which the serialized data is consumed by the TensorFlow runtime.
The technical implementation of this buffer overflow occurs within TensorFlow's internal graph processing mechanisms where graph definitions are parsed and executed. When TensorFlow encounters a serialized graph containing oversized data structures, particularly within node attributes or tensor specifications, the system attempts to allocate memory buffers based on potentially malicious size parameters. This flaw aligns with CWE-121, which describes stack-based buffer overflow conditions, and CWE-122, which covers heap-based buffer overflow scenarios. The vulnerability can be exploited through various attack vectors including model loading from untrusted sources, remote execution of malicious graphs, or manipulation of training data that gets processed through TensorFlow's graph execution engine. The attack typically requires an attacker to craft a specially formatted graph definition that triggers the overflow condition during graph parsing and execution phases.
The operational impact of CVE-2018-7575 extends beyond simple denial of service scenarios, as successful exploitation could enable remote code execution on systems running vulnerable TensorFlow versions. This vulnerability particularly affects environments where TensorFlow is used to process untrusted machine learning models or data inputs, including cloud computing platforms, research institutions, and enterprise AI deployments. The buffer overflow could allow attackers to execute arbitrary code with the privileges of the TensorFlow process, potentially leading to complete system compromise. Organizations utilizing TensorFlow for model serving, training pipelines, or automated machine learning workflows face heightened risk, especially when these systems process inputs from external sources without proper validation. The vulnerability also impacts containerized environments and serverless computing platforms where TensorFlow is deployed as part of larger applications, as the exploitation could propagate across multiple system components.
Mitigation strategies for CVE-2018-7575 should prioritize immediate version upgrades to TensorFlow 1.8.0 or later, where the buffer overflow has been addressed through enhanced input validation and proper bounds checking mechanisms. System administrators should implement strict input validation policies for all graph definitions and model files processed by TensorFlow, particularly those originating from external sources or untrusted parties. Network segmentation and access controls should be enforced to limit exposure of TensorFlow services to potentially malicious inputs, while implementing monitoring solutions to detect anomalous behavior during graph processing. The remediation process should include comprehensive testing of updated TensorFlow installations to ensure that the buffer overflow protections are functioning correctly and that existing applications continue to operate as expected. Organizations should also consider implementing runtime protection mechanisms such as address space layout randomization and stack canaries to provide additional defense-in-depth measures against potential exploitation attempts. Security teams should review their incident response procedures to account for potential exploitation of this vulnerability and establish protocols for rapid deployment of patches across their TensorFlow-based infrastructure.