CVE-2019-9635 in TensorFlow
Summary
by MITRE
NULL pointer dereference in Google TensorFlow before 1.12.2 could cause a denial of service via an invalid GIF file.
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Analysis
by VulDB Data Team • 09/06/2023
The vulnerability CVE-2019-9635 represents a critical NULL pointer dereference flaw within Google TensorFlow's image processing capabilities, specifically affecting versions prior to 1.12.2. This issue stems from inadequate input validation when processing GIF image files, creating a scenario where malformed or invalid GIF data can trigger a system crash. The vulnerability resides in the image decoding module that handles GIF format parsing, where the software fails to properly validate the structure and content of incoming GIF files before attempting to dereference pointers within the parsed data structure. According to CWE-476, this weakness falls under NULL pointer dereference, a fundamental programming error that occurs when software attempts to access memory through a pointer that has not been properly initialized to a valid memory address.
The technical exploitation of this vulnerability occurs when TensorFlow processes an invalid GIF file that contains malformed headers or corrupted image data structures. During the parsing process, the software attempts to access a pointer that remains NULL due to the invalid file structure, resulting in an immediate system crash or denial of service condition. This type of vulnerability is particularly dangerous in production environments where TensorFlow serves as a core component for machine learning applications, as it can be triggered by simply uploading or processing a malicious GIF file. The flaw demonstrates poor defensive programming practices where error handling mechanisms fail to account for malformed input data, creating a direct path to system instability.
Operationally, this vulnerability poses significant risks to organizations relying on TensorFlow for image processing tasks, particularly in web applications, content management systems, or any platform that accepts user-uploaded images. Attackers can exploit this weakness by crafting a specially designed GIF file that triggers the NULL pointer dereference, causing the affected TensorFlow instance to crash and become unavailable for legitimate requests. This denial of service condition can be particularly devastating in cloud environments or distributed systems where TensorFlow serves multiple concurrent users, as a single malicious file can disrupt service availability for all users of that system. The impact extends beyond simple service disruption, as it can be used as a vector for more sophisticated attacks targeting system availability and reliability.
Mitigation strategies for CVE-2019-9635 focus primarily on upgrading to TensorFlow version 1.12.2 or later, where the vulnerability has been addressed through improved input validation and error handling mechanisms. Organizations should implement comprehensive input sanitization protocols that validate image file structures before processing, including checking for proper GIF file headers and ensuring all pointer references are properly initialized. Additionally, deploying network-level firewalls and content filtering systems can help prevent malicious GIF files from reaching TensorFlow processing components. The remediation approach aligns with ATT&CK technique T1499.004, which addresses denial of service through system resource exhaustion, as the vulnerability effectively consumes system resources through crashes rather than traditional resource depletion methods. Security teams should also implement monitoring solutions to detect unusual crash patterns or service disruptions that may indicate exploitation attempts.