CVE-2021-41225 in TensorFlow
Summary
by MITRE • 11/06/2021
TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's Grappler optimizer has a use of unitialized variable. If the `train_nodes` vector (obtained from the saved model that gets optimized) does not contain a `Dequeue` node, then `dequeue_node` is left unitialized. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 11/10/2021
The vulnerability identified as CVE-2021-41225 resides within TensorFlow's Grappler optimizer component, a critical element responsible for optimizing computational graphs during machine learning model training and inference processes. This issue manifests as a use of uninitialized variable condition that can potentially compromise the integrity and stability of machine learning workflows. The Grappler optimizer operates by analyzing and modifying computational graphs to improve performance, but in this specific case, it fails to properly initialize a crucial variable under certain conditions, creating a potential attack surface for malicious actors seeking to exploit the system.
The technical flaw occurs within the Grappler optimization logic when processing saved models that contain training nodes. Specifically, when the optimizer examines the `train_nodes` vector obtained from a saved model, it expects to find a `Dequeue` node to properly initialize the `dequeue_node` variable. However, if this `Dequeue` node is absent from the `train_nodes` vector, the `dequeue_node` variable remains uninitialized, leading to undefined behavior that can result in crashes, incorrect computations, or potentially exploitable conditions. This uninitialized variable scenario falls under CWE-457: Use of Uninitialized Variable, which represents a fundamental programming error that can lead to unpredictable system behavior and security implications.
The operational impact of this vulnerability extends beyond simple functional failures, potentially affecting the reliability and security posture of machine learning systems built on TensorFlow. When the Grappler optimizer encounters a model without the expected `Dequeue` node, the uninitialized variable can cause the optimizer to behave unpredictably, potentially leading to incorrect model optimization decisions or system instability. This risk is particularly concerning in production environments where TensorFlow-based machine learning systems process sensitive data or support critical business operations. The vulnerability affects multiple versions of TensorFlow including 2.4.4, 2.5.2, 2.6.1, and the affected versions prior to the 2.7.0 release, indicating a widespread impact across the TensorFlow ecosystem. This aligns with ATT&CK technique T1203: Exploitation for Credential Access, as uninitialized variables can potentially be leveraged to execute arbitrary code or manipulate system behavior through controlled inputs.
The remediation strategy involves implementing proper initialization of the `dequeue_node` variable regardless of whether a `Dequeue` node exists in the `train_nodes` vector. This fix ensures that the Grappler optimizer maintains consistent behavior and predictable operation even when encountering models with varying node configurations. The TensorFlow development team has addressed this issue by including the fix in TensorFlow 2.7.0 and cherry-picking it to maintain backward compatibility with the supported release lines 2.6.1, 2.5.2, and 2.4.4. Organizations using affected TensorFlow versions should immediately implement these updates to protect their machine learning infrastructure from potential exploitation attempts that could leverage this uninitialized variable vulnerability to compromise system integrity or disrupt machine learning workflows.