CVE-2021-41221 in TensorFlow
Summary
by MITRE • 11/06/2021
TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for the `Cudnn*` operations in TensorFlow can be tricked into accessing invalid memory, via a heap buffer overflow. This occurs because the ranks of the `input`, `input_h` and `input_c` parameters are not validated, but code assumes they have certain values. 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 CVE-2021-41221 affects TensorFlow, a widely-used open source machine learning platform that processes complex data through neural networks and deep learning models. This security flaw specifically targets the Cudnn* operations within TensorFlow's computational framework, which are designed to accelerate deep learning computations using NVIDIA's cuDNN library. The issue stems from inadequate validation of input parameter dimensions during shape inference operations, creating a critical memory safety gap that could be exploited by malicious actors to compromise system integrity.
The technical flaw manifests as a heap buffer overflow in TensorFlow's shape inference code for Cudnn operations. This occurs when the system processes input tensors with insufficient validation of the ranks of three critical parameters: input, input_h, and input_c. These parameters represent different tensor dimensions that must conform to specific expected values for proper operation. However, the code fails to verify that these parameters meet the required dimensional constraints before proceeding with memory operations. This assumption-based approach leads to memory access violations when the code attempts to read or write beyond allocated buffer boundaries, potentially resulting in arbitrary code execution or system crashes.
The operational impact of this vulnerability extends across multiple TensorFlow versions, affecting releases from 2.4.4 through 2.6.1, with the fix scheduled for inclusion in TensorFlow 2.7.0. The vulnerability's exploitation potential is significant because it occurs within core computational operations that are fundamental to machine learning workflows, particularly in environments that rely heavily on GPU acceleration through cuDNN. Attackers could potentially craft malicious input data that triggers the buffer overflow condition, leading to denial of service attacks or more severe compromise of systems running affected TensorFlow versions. The vulnerability affects systems processing large-scale machine learning models where Cudnn operations are frequently invoked, making it particularly dangerous in production environments.
Security mitigations for CVE-2021-41221 include immediate deployment of TensorFlow version 2.7.0 or applying the cherry-picked fixes to affected versions 2.6.1, 2.5.2, and 2.4.4. Organizations should prioritize updating their TensorFlow installations to prevent exploitation, particularly in environments handling sensitive data or critical machine learning workloads. The vulnerability aligns with CWE-121, heap-based buffer overflow, and can be categorized under ATT&CK technique T1059 for command and control through code execution. System administrators should implement monitoring for unusual memory access patterns and ensure comprehensive testing of updated TensorFlow installations before deployment to maintain operational continuity while addressing the security gap.