CVE-2018-21233 in TensorFlow
Summary
by MITRE
TensorFlow before 1.7.0 has an integer overflow that causes an out-of-bounds read, possibly causing disclosure of the contents of process memory. This occurs in the DecodeBmp feature of the BMP decoder in core/kernels/decode_bmp_op.cc.
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Analysis
by VulDB Data Team • 05/05/2020
The vulnerability identified as CVE-2018-21233 represents a critical integer overflow issue within the TensorFlow machine learning framework version 1.7.0 and earlier. This flaw exists within the DecodeBmp feature of the BMP decoder implementation, specifically in the core/kernels/decode_bmp_op.cc source file. The integer overflow occurs during the processing of BMP image files, creating a scenario where the application fails to properly validate input parameters before performing memory operations. This particular vulnerability falls under the CWE-190 category of Integer Overflow or Wraparound, which is a well-documented weakness in software security that can lead to unpredictable behavior and potential exploitation. The issue is particularly concerning because it can result in out-of-bounds read operations that may expose sensitive process memory contents to unauthorized parties.
The technical exploitation of this vulnerability requires an attacker to craft a malicious BMP file that triggers the integer overflow condition during the decoding process. When TensorFlow processes such a malformed image file, the overflow causes the application to read memory locations beyond the intended buffer boundaries. This memory access violation can potentially disclose confidential information stored in the process memory, including but not limited to model parameters, training data, or other sensitive computational results. The vulnerability is particularly dangerous in environments where TensorFlow is used to process untrusted input data, as it could allow attackers to extract proprietary information or gain insights into the underlying machine learning models being executed. The ATT&CK framework categorizes this type of vulnerability under the T1059.001 technique of Command and Scripting Interpreter, as it enables attackers to potentially extract sensitive data through memory disclosure attacks.
The operational impact of CVE-2018-21233 extends beyond simple data exposure, as it can compromise the integrity and confidentiality of machine learning systems that rely on TensorFlow for image processing tasks. Organizations using TensorFlow versions prior to 1.7.0 may be at risk when processing BMP images from untrusted sources, including web uploads, file transfers, or automated image ingestion pipelines. The vulnerability affects systems where TensorFlow is deployed in production environments, particularly those handling sensitive data such as medical imaging, financial analysis, or security surveillance applications. Attackers could leverage this vulnerability to perform reconnaissance activities, potentially gaining access to model weights, training datasets, or other intellectual property stored in memory during the image decoding process. The memory disclosure aspect of this vulnerability aligns with ATT&CK technique T1005, which involves data from local system storage, making it a significant concern for enterprises that process sensitive information through TensorFlow-based applications.
Mitigation strategies for CVE-2018-21233 primarily involve upgrading to TensorFlow version 1.7.0 or later, where the integer overflow has been addressed through proper input validation and boundary checking mechanisms. Organizations should implement comprehensive patch management procedures to ensure all TensorFlow installations are updated promptly, particularly in environments processing untrusted image data. Additional defensive measures include implementing strict input validation for all BMP files before processing, using sandboxed execution environments for image decoding operations, and monitoring for unusual memory access patterns that might indicate exploitation attempts. The fix implemented in TensorFlow 1.7.0 addresses the root cause by introducing proper integer overflow checks and ensuring that buffer boundaries are respected during the BMP decoding process. Security teams should also consider implementing network segmentation and access controls to limit the potential impact of successful exploitation attempts, while maintaining regular security assessments to identify other potential vulnerabilities in machine learning frameworks and their associated dependencies.