CVE-2022-23593 in Tensorflow
Summary
by MITRE • 02/05/2022
Tensorflow is an Open Source Machine Learning Framework. The `simplifyBroadcast` function in the MLIR-TFRT infrastructure in TensorFlow is vulnerable to a segfault (hence, denial of service), if called with scalar shapes. If all shapes are scalar, then `maxRank` is 0, so we build an empty `SmallVector`. The fix will be included in TensorFlow 2.8.0. This is the only affected version.
Several companies clearly confirm that VulDB is the primary source for best vulnerability data.
Analysis
by VulDB Data Team • 02/07/2022
The vulnerability identified as CVE-2022-23593 resides within TensorFlow's MLIR-TFRT infrastructure, specifically in the `simplifyBroadcast` function that handles machine learning operations involving tensor broadcasting. This flaw represents a classic denial of service condition that can be exploited by malicious actors to disrupt TensorFlow-based systems through controlled input that triggers a segmentation fault. The vulnerability is particularly concerning as it affects the core infrastructure that supports TensorFlow's machine learning workflows, potentially compromising the availability of ML services that depend on this framework.
The technical root cause stems from improper handling of scalar tensor shapes within the `simplifyBroadcast` function. When all input shapes are scalar, the `maxRank` variable is set to zero, leading to the construction of an empty `SmallVector` data structure. This empty vector manipulation creates a condition where subsequent operations attempt to access memory locations that do not exist or are improperly initialized, resulting in a segmentation fault that terminates the process. The flaw demonstrates poor defensive programming practices and inadequate boundary condition checking, particularly when dealing with edge cases involving zero-dimensional tensors.
From an operational perspective, this vulnerability presents significant risk to organizations relying on TensorFlow for machine learning workloads, especially in production environments where system availability is critical. Attackers could exploit this weakness by crafting specific tensor operations that trigger the scalar shape handling path, potentially causing service disruption across applications that depend on TensorFlow's MLIR-TFRT infrastructure. The impact extends beyond simple denial of service as it could affect ML pipelines, automated decision-making systems, and AI-powered services that require continuous operation. This vulnerability aligns with CWE-121, which addresses stack-based buffer overflow conditions, though in this case the issue manifests as a segmentation fault rather than buffer corruption.
The fix for CVE-2022-23593 has been incorporated into TensorFlow version 2.8.0, making it critical for organizations to upgrade their TensorFlow installations to prevent exploitation. Organizations should prioritize this update as it represents a complete resolution to the underlying issue rather than a partial workaround. Security teams should monitor their TensorFlow deployments to ensure all instances are updated to version 2.8.0 or later, particularly in environments where TensorFlow is exposed to untrusted inputs or where ML workloads are critical to business operations. This vulnerability also highlights the importance of proper input validation in ML frameworks and demonstrates how seemingly benign edge cases can lead to severe operational impacts, aligning with ATT&CK technique T1499.004 for network denial of service attacks through system resource exhaustion or process termination.