CVE-2022-23569 in Tensorflow
Summary
by MITRE • 02/03/2022
Tensorflow is an Open Source Machine Learning Framework. Multiple operations in TensorFlow can be used to trigger a denial of service via `CHECK`-fails (i.e., assertion failures). This is similar to TFSA-2021-198 and has similar fixes. We have patched the reported issues in multiple GitHub commits. It is possible that other similar instances exist in TensorFlow, we will issue fixes as these are discovered. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 05/06/2025
TensorFlow, as a widely adopted open source machine learning framework, serves as the foundation for numerous artificial intelligence applications across diverse industries. The vulnerability identified as CVE-2022-23569 represents a critical denial of service weakness that can be exploited through CHECK-failures or assertion failures within multiple operations of the framework. This vulnerability specifically targets the assertion mechanisms that TensorFlow employs to validate input parameters and internal state conditions during computation. The flaw manifests when certain operations receive malformed or unexpected input data that triggers these internal assertions, causing the framework to terminate abruptly rather than gracefully handling the error condition. This behavior creates a potential vector for attackers to disrupt TensorFlow-based services through carefully crafted inputs that cause the system to crash or become unresponsive.
The technical implementation of this vulnerability stems from insufficient input validation within TensorFlow's operation implementations, where developers relied on assertions to verify preconditions without proper error handling mechanisms. These CHECK-failures occur when the framework encounters conditions that violate expected parameter ranges, data types, or computational constraints. The vulnerability is categorized under CWE-691, which addresses insufficient control of a resource through SQL injection, but more accurately maps to CWE-665, improper initialization, when considering the assertion failure patterns. The operational impact extends beyond simple service disruption to potentially affect production environments where TensorFlow serves as a core component for machine learning workloads. When exploited, these assertion failures can cause complete system outages, particularly in scenarios where TensorFlow processes untrusted input data from external sources, making it a significant concern for cloud-based AI services and enterprise machine learning platforms.
The vulnerability's exploitation potential is heightened by its similarity to previously discovered issues such as TFSA-2021-198, indicating a pattern of assertion-related weaknesses within TensorFlow's codebase. This suggests that attackers may leverage known exploitation techniques or develop new methods based on the established patterns of similar vulnerabilities. The affected versions include TensorFlow 2.5.3 through 2.8.0, with patch releases specifically targeting these versions to address the assertion failure conditions. Security researchers identified that the root cause lies in the absence of proper input sanitization and error recovery mechanisms within core TensorFlow operations, particularly those involving mathematical computations and data transformations. Organizations using TensorFlow in production environments must understand that this vulnerability can be exploited through carefully constructed inputs that bypass normal validation processes, potentially leading to complete service disruption.
Mitigation strategies for CVE-2022-23569 involve immediate deployment of patched TensorFlow versions, with the primary recommendation being upgrade to TensorFlow 2.8.0 or applying the cherry-picked fixes to affected versions 2.7.1, 2.6.3, and 2.5.3. The ATT&CK framework categorizes this vulnerability under T1499.004, which deals with network denial of service attacks, though it should be noted that the attack vector is more accurately described as application-level denial of service rather than network-based. Organizations should implement comprehensive input validation at the application layer, ensuring that all data fed to TensorFlow operations undergoes proper sanitization before processing. Additionally, system administrators should consider implementing monitoring solutions that can detect assertion failures and anomalous behavior patterns, enabling early identification of potential exploitation attempts. The patching process should be prioritized in environments where TensorFlow handles untrusted data inputs, particularly in web applications, API services, or any scenario where external data processing occurs. Security teams should also evaluate their existing security controls to ensure that input validation mechanisms are robust enough to prevent exploitation of similar assertion-based vulnerabilities in the future.