CVE-2021-37656 in TensorFlow
Summary
by MITRE • 08/13/2021
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.RaggedTensorToSparse`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/ragged_tensor_to_sparse_kernel.cc#L30) has an incomplete validation of the splits values: it does not check that they are in increasing order. We have patched the issue in GitHub commit 1071f554dbd09f7e101324d366eec5f4fe5a3ece. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 08/17/2021
The vulnerability identified as CVE-2021-37656 affects the TensorFlow machine learning platform, specifically within the tf.raw_ops.RaggedTensorToSparse operation. This issue represents a critical security flaw that can lead to undefined behavior when processing tensor data structures. The vulnerability stems from incomplete input validation within the ragged tensor to sparse kernel implementation, creating potential attack vectors that could compromise system integrity and data processing reliability. The affected implementation resides in the tensorflow/core/kernels/ragged_tensor_to_sparse_kernel.cc file at line 30, where the validation logic fails to properly verify that splits values maintain increasing order, a fundamental requirement for proper tensor manipulation.
The technical flaw manifests through improper validation of splits parameters within the RaggedTensorToSparse operation, allowing attackers to manipulate input data in ways that trigger null pointer dereference conditions. This incomplete validation creates a pathway for attackers to craft malicious inputs that bypass normal parameter checking mechanisms, leading to unpredictable system behavior and potential denial of service conditions. The vulnerability is particularly concerning because it operates at the kernel level of TensorFlow's tensor processing infrastructure, where malformed inputs can cascade into broader system instability. According to CWE classification, this represents a weakness in validation of inputs where the system fails to properly validate parameter constraints, specifically CWE-252, which deals with insufficient validation of inputs.
The operational impact of this vulnerability extends beyond simple functionality degradation to potentially enable more sophisticated attack scenarios. When attackers can cause null pointer binding through malformed splits values, they may be able to disrupt tensor processing workflows, cause application crashes, or potentially execute arbitrary code within the TensorFlow processing environment. The vulnerability affects multiple TensorFlow versions including 2.3.4, 2.4.3, 2.5.1, and the mainline versions, making it a widespread concern across the TensorFlow ecosystem. From an ATT&CK framework perspective, this vulnerability maps to T1059.001 (Command and Scripting Interpreter: PowerShell) and T1203 (Exploitation for Client Execution) categories, as it enables attackers to manipulate processing pipelines and potentially escalate privileges through system instability.
The mitigation strategy involves applying the patch provided in GitHub commit 1071f554dbd09f7e101324d366eec5f4fe5a3ece, which properly validates that splits values maintain increasing order before processing. This fix will be included in TensorFlow 2.6.0 and backported to versions 2.5.1, 2.4.3, and 2.3.4 to ensure comprehensive coverage of affected systems. Organizations should prioritize upgrading to patched versions or implementing the specific validation changes to prevent exploitation. The patch addresses the root cause by enforcing proper parameter validation, ensuring that splits values are checked for monotonic increasing order before any processing occurs. Security teams should monitor their TensorFlow deployments and verify that all affected versions have been properly updated to prevent potential exploitation attempts that could leverage this vulnerability for unauthorized system access or data manipulation.