CVE-2022-23595 in Tensorflow
Summary
by MITRE • 02/05/2022
Tensorflow is an Open Source Machine Learning Framework. When building an XLA compilation cache, if default settings are used, TensorFlow triggers a null pointer dereference. In the default scenario, all devices are allowed, so `flr->config_proto` is `nullptr`. 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 • 02/07/2022
The vulnerability described in CVE-2022-23595 represents a critical null pointer dereference issue within the TensorFlow machine learning framework that specifically affects the XLA compilation cache functionality. This flaw exists in the way TensorFlow handles configuration parameters during the compilation process, creating a scenario where the system attempts to dereference a null pointer when processing device configurations. The issue manifests when default settings are employed during XLA compilation, which is a common operational practice in many machine learning environments. The root cause stems from the assumption that `flr->config_proto` will always contain valid configuration data, but in default scenarios where all devices are permitted, this pointer remains uninitialized and null, leading to immediate system termination upon dereference.
The technical exploitation of this vulnerability occurs within the XLA (Accelerated Linear Algebra) compilation subsystem of TensorFlow, which is designed to optimize machine learning computations across various hardware platforms. When TensorFlow processes XLA compilation requests with default configurations, the code path fails to properly validate whether the configuration protobuf structure has been initialized before attempting to access its members. This particular flaw falls under the CWE-476 category of Null Pointer Dereference, which is classified as a fundamental programming error that can lead to application crashes and potentially provide attackers with opportunities for denial of service attacks. The vulnerability is particularly concerning because XLA compilation is a core component of TensorFlow's performance optimization capabilities, making it likely to be invoked in production environments.
The operational impact of CVE-2022-23595 extends beyond simple application crashes, as it can effectively render TensorFlow installations unusable in scenarios where XLA compilation is automatically triggered. This affects not only development environments but also production systems that rely on TensorFlow for machine learning workloads, potentially causing widespread service disruptions across organizations using the framework. The vulnerability affects multiple versions of TensorFlow including 2.5.3, 2.6.3, 2.7.1, and the affected versions are actively maintained, indicating that the issue has significant implications for organizations planning their software update strategies. The fix implementation involves proper null pointer validation before accessing the configuration protobuf structure, which aligns with standard secure coding practices and defensive programming principles. Organizations utilizing TensorFlow for critical machine learning operations should prioritize immediate patching to prevent potential service interruptions.
Mitigation strategies for this vulnerability should include immediate deployment of the patched TensorFlow versions 2.8.0, 2.7.1, 2.6.3, and 2.5.3 as recommended by the maintainers. System administrators should also consider implementing monitoring solutions to detect potential exploitation attempts through abnormal application behavior or crash patterns. The vulnerability demonstrates the importance of proper input validation and defensive programming practices in complex software systems, particularly in frameworks that handle sensitive computational workloads. Organizations should also review their TensorFlow deployment configurations to ensure that default settings are not inadvertently triggering the vulnerable code paths. From a security posture perspective, this vulnerability aligns with ATT&CK technique T1499.004 for network denial of service, as it can cause service disruption through application crashes, though it does not appear to enable more sophisticated attack vectors such as code execution or privilege escalation. The issue highlights the necessity of comprehensive testing of default configurations and the importance of validating all pointer dereferences in production software systems.