CVE-2021-29520 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. 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 CVE-2021-29520 affects TensorFlow, a widely-used open-source machine learning platform that serves as the foundation for numerous artificial intelligence applications across industries. This security flaw resides within the Conv3DBackprop* operations, which are essential components for computing gradients in three-dimensional convolutional neural networks. The issue stems from insufficient input validation mechanisms that fail to properly verify the dimensional compatibility between critical tensor arguments. When these operations process malformed input data, they can execute beyond allocated memory boundaries, leading to heap buffer overflows that compromise system stability and potentially enable arbitrary code execution.
The technical implementation flaw manifests in the conv_grad_shape_utils.cc file where the TensorFlow kernel assumes that input tensors, filter sizes, and output backpropagation tensors maintain identical dimensional structures. This assumption becomes problematic when processing tensors with mismatched shapes, as the code accesses these arrays in parallel without proper validation checks. The vulnerability specifically affects the tf.raw_ops.Conv3DBackprop* operations that are used during the backward pass of three-dimensional convolution operations in neural network training. The code path from lines 94 to 153 in the implementation demonstrates the lack of proper bounds checking and shape validation that would normally occur before array access operations. This design flaw aligns with CWE-129, which addresses insufficient validation of array indices, and represents a classic buffer overflow vulnerability that can be exploited through carefully crafted malicious inputs.
The operational impact of this vulnerability extends across numerous machine learning applications that rely on TensorFlow's convolution operations, particularly those involving 3D data processing such as medical imaging analysis, video processing, and volumetric data analysis. Attackers could potentially exploit this vulnerability by feeding specially crafted tensor inputs that cause the heap buffer overflow during gradient computation, leading to denial of service conditions or potentially more severe consequences including remote code execution. The vulnerability affects multiple TensorFlow versions including 2.1.4, 2.2.3, 2.3.3, 2.4.2, and the affected versions are still within their supported lifecycle periods, making this issue particularly concerning for organizations maintaining legacy systems. The exploitation of this vulnerability would align with ATT&CK technique T1059.001 for command and control through code injection, and T1499.004 for network denial of service attacks, as the heap corruption could be leveraged to disrupt system operations.
Organizations utilizing TensorFlow should immediately implement mitigations including upgrading to TensorFlow 2.5.0 or applying the cherry-picked fixes to affected versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4. The recommended approach involves verifying tensor shapes before invoking Conv3DBackprop* operations and implementing additional input validation layers that ensure dimensional compatibility between input tensors, filter sizes, and output backpropagation tensors. System administrators should also consider implementing runtime monitoring to detect anomalous tensor processing patterns that could indicate exploitation attempts. The fix addresses the core issue by introducing proper validation checks that verify tensor dimensional compatibility before memory access operations, preventing the heap buffer overflow conditions that were previously possible. This vulnerability underscores the importance of input validation in machine learning frameworks and demonstrates how seemingly routine mathematical operations can become security risks when proper bounds checking is omitted from critical computational paths.