CVE-2022-36004 in TensorFlow
Summary
by MITRE • 09/17/2022
TensorFlow is an open source platform for machine learning. When `tf.random.gamma` receives large input shape and rates, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit 552bfced6ce4809db5f3ca305f60ff80dd40c5a3. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
If you want to get best quality of vulnerability data, you may have to visit VulDB.
Analysis
by VulDB Data Team • 09/17/2022
The vulnerability identified as CVE-2022-36004 affects TensorFlow, a widely adopted open source machine learning platform that serves as the foundation for numerous artificial intelligence applications across various industries. This issue specifically targets the tf.random.gamma function, which generates random samples from the gamma distribution, a fundamental operation in probabilistic modeling and statistical inference. The flaw manifests when the function processes large input shapes and rates, creating a critical condition that results in a CHECK failure mechanism triggering a denial of service condition. This represents a significant security concern as it can be exploited by malicious actors to disrupt TensorFlow-based services and applications through controlled input parameters that cause the system to crash or become unresponsive.
The technical implementation of this vulnerability stems from inadequate parameter validation within the tf.random.gamma function's internal logic. When processing large tensor dimensions and rate parameters, the function fails to properly handle boundary conditions, leading to a CHECK assertion failure that terminates the execution process. This behavior aligns with CWE-617, which addresses reachable assertions, where defensive programming checks become exploitable when they fail to account for malicious or unexpected inputs. The vulnerability demonstrates a classic denial of service scenario where controlled input parameters can cause the system to enter an unrecoverable state, making the service unavailable to legitimate users. The CHECK failure mechanism, designed to catch programming errors during development, becomes a vector for exploitation when input validation is insufficient.
The operational impact of CVE-2022-36004 extends beyond simple service disruption to potentially affect production machine learning environments that rely on TensorFlow for critical operations. Organizations using TensorFlow versions 2.7.2 through 2.9.1 face immediate risk as these releases remain within the supported maintenance window, making them prime targets for exploitation. The vulnerability affects systems running TensorFlow-based applications in cloud environments, edge computing deployments, and enterprise AI platforms where continuous availability is paramount. Attackers could exploit this weakness to cause service outages, leading to business disruption, loss of productivity, and potential financial impact. The lack of known workarounds forces organizations to either upgrade to patched versions or risk continued exposure to this denial of service vulnerability.
The remediation strategy for this vulnerability involves applying the patch included in GitHub commit 552bfced6ce4809db5f3ca305f60ff80dd40c5a3, which has been integrated into TensorFlow 2.10.0 and backported to the affected 2.9.1, 2.8.1, and 2.7.2 releases. This fix addresses the core validation issue in the tf.random.gamma function by implementing proper boundary checks and input parameter handling. Organizations should prioritize updating their TensorFlow installations to the patched versions to eliminate the risk of exploitation. The ATT&CK framework categorizes this vulnerability under T1499.004, which covers network denial of service, as the exploitation results in service unavailability. Additionally, the vulnerability demonstrates characteristics of T1595.001, reconnaissance techniques, as attackers may need to identify systems running vulnerable TensorFlow versions before exploitation. System administrators should implement monitoring to detect potential exploitation attempts and ensure that all TensorFlow deployments are updated to versions containing the patched commit to maintain operational security and prevent service disruption.