CVE-2018-7576 in TensorFlow
Summary
by MITRE
Google TensorFlow 1.6.x and earlier is affected by: Null Pointer Dereference. The type of exploitation is: context-dependent.
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Analysis
by VulDB Data Team • 06/01/2020
The vulnerability identified as CVE-2018-7576 represents a critical null pointer dereference flaw within Google TensorFlow versions 1.6.x and earlier. This issue manifests as a denial of service condition that can be exploited by malicious actors to disrupt the normal operation of TensorFlow-based applications and systems. The vulnerability exists in the core tensor processing components of the machine learning framework, where improper input validation leads to unhandled null pointer references during tensor operations. The context-dependent nature of this exploitation means that the vulnerability can be triggered under specific conditions related to the input data or operational environment, making it particularly challenging to predict and prevent.
The technical implementation of this flaw occurs within TensorFlow's computational graph execution engine where tensors are processed through various operations. When certain malformed or unexpected tensor inputs are encountered during graph execution, the framework fails to properly validate pointer references before dereferencing them, leading to system crashes or abnormal termination. This type of vulnerability falls under CWE-476 which specifically addresses null pointer dereference conditions in software implementations. The flaw demonstrates poor error handling practices within the tensor processing pipeline and indicates insufficient input sanitization mechanisms that should be in place to prevent such conditions from occurring during runtime execution.
The operational impact of CVE-2018-7576 extends beyond simple service disruption to potentially compromise the integrity of machine learning workflows and data processing pipelines. Attackers can leverage this vulnerability to cause denial of service against TensorFlow applications, which may be critical components in production environments, cloud services, or AI-powered systems. In scenarios where TensorFlow is used for processing sensitive data or supporting automated decision-making systems, such exploitation could lead to significant business disruption and potential data loss. The vulnerability is particularly concerning in distributed computing environments where TensorFlow serves as a foundational component for large-scale machine learning operations, as it can cascade through interconnected systems and affect multiple services simultaneously.
Mitigation strategies for this vulnerability require immediate patching of affected TensorFlow installations to versions 1.7.0 or later where the null pointer dereference issue has been addressed. Organizations should implement comprehensive input validation mechanisms and sanitize all tensor data before processing to prevent exploitation of this vulnerability. Security teams should also consider deploying runtime monitoring solutions that can detect anomalous tensor processing patterns indicative of attempted exploitation. The ATT&CK framework categorizes this type of vulnerability under the 'Execution' phase where adversaries leverage software flaws to gain system access or cause service disruption. Additionally, implementing proper access controls and network segmentation can limit the potential impact of exploitation attempts, while regular security assessments should be conducted to identify similar vulnerabilities in related software components. System administrators should also establish robust incident response procedures specifically designed to handle denial of service conditions resulting from null pointer dereference vulnerabilities in machine learning frameworks.