CVE-2021-29524 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/496c2630e51c1a478f095b084329acedb253db6b/tensorflow/core/kernels/conv_grad_shape_utils.cc#L130) does a modulus operation where the divisor is controlled by the caller. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 05/16/2021
The vulnerability identified as CVE-2021-29524 affects TensorFlow, a widely-used open-source machine learning platform that serves as the foundation for numerous artificial intelligence applications across various industries. This security flaw resides within the tf.raw_ops.Conv2DBackpropFilter operation, which is part of TensorFlow's convolutional neural network implementations. The issue manifests as a division by zero condition that can be triggered by malicious input, potentially compromising the stability and security of machine learning systems that rely on TensorFlow for their operations.
The technical root cause of this vulnerability stems from an implementation flaw in the convolution gradient shape computation logic within TensorFlow's kernel components. Specifically, the problematic code located at tensorflow/core/kernels/conv_grad_shape_utils.cc line 130 performs a modulus operation where the divisor is directly controlled by user input parameters. This design creates a scenario where an attacker can manipulate the input values to cause the divisor to become zero, resulting in a division by zero error that crashes the application or potentially allows for further exploitation. This type of vulnerability falls under the Common Weakness Enumeration category CWE-369, which specifically addresses the division by zero weakness that can lead to application crashes or denial of service conditions.
The operational impact of this vulnerability extends beyond simple system instability, as it represents a critical security concern for organizations relying on TensorFlow-based machine learning systems. When exploited, the division by zero condition can cause complete application crashes, potentially leading to denial of service scenarios that disrupt machine learning workflows and data processing pipelines. In production environments where TensorFlow is used for critical applications such as autonomous vehicles, financial services, healthcare diagnostics, or security monitoring systems, such a vulnerability could result in significant operational disruptions and potential data integrity issues. The vulnerability affects multiple versions of TensorFlow including 2.4.2, 2.3.3, 2.2.3, and 2.1.4, indicating that it has been present in the codebase for several releases and affects a substantial portion of the TensorFlow user base.
The remediation approach for CVE-2021-29524 involves implementing proper input validation and bounds checking to prevent the divisor from reaching zero values during the modulus operation. The TensorFlow development team has addressed this issue by incorporating the fix into TensorFlow 2.5.0 and has also cherry-picked the commit to maintain backward compatibility with older supported versions. Organizations should prioritize updating their TensorFlow installations to the patched versions to eliminate this vulnerability. Additionally, system administrators should implement monitoring solutions to detect potential exploitation attempts and consider implementing additional input validation layers at the application level. The fix aligns with the ATT&CK framework's defense evasion techniques, as it addresses a fundamental implementation weakness that could be leveraged by attackers to disrupt services through controlled input manipulation, thereby preventing potential lateral movement or persistent access attempts that might follow such initial exploitation.