CVE-2022-36005 in TensorFlow
Summary
by MITRE • 09/17/2022
TensorFlow is an open source platform for machine learning. When `tf.quantization.fake_quant_with_min_max_vars_gradient` receives input `min` or `max` that is nonscalar, it gives a `CHECK` fail that can trigger a denial of service attack. We have patched the issue in GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed. 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.
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Analysis
by VulDB Data Team • 10/20/2022
The vulnerability CVE-2022-36005 affects TensorFlow, a widely-used open-source machine learning platform that serves as the foundation for numerous artificial intelligence applications across various industries. This security flaw specifically targets the `tf.quantization.fake_quant_with_min_max_vars_gradient` function, which is employed in the quantization process of neural network models to reduce computational requirements and memory usage. The issue arises when the function receives input parameters for `min` or `max` that are not scalar values, leading to a critical check failure that can be exploited to cause system disruption.
The technical flaw manifests as a CHECK failure within TensorFlow's quantization gradient computation mechanism, where the system expects scalar inputs for the min and max parameters but encounters nonscalar tensors. This mismatch triggers an assertion failure that terminates the process rather than gracefully handling the invalid input. The vulnerability falls under CWE-248, an unspecified CWE related to an exception being thrown from a library function, and represents a classic denial of service scenario where an attacker can craft malicious input to crash the application. The implementation does not properly validate input types before proceeding with operations, creating an execution path that leads to immediate system termination.
The operational impact of this vulnerability is significant for organizations relying on TensorFlow for machine learning workloads, particularly those processing untrusted data inputs. Attackers can exploit this weakness by providing nonscalar min or max values to the quantization function, causing the TensorFlow process to crash and resulting in denial of service for legitimate users. This vulnerability affects multiple TensorFlow versions including 2.7.2, 2.8.1, 2.9.1, and the affected versions are still within their supported release cycles, making the potential attack surface particularly concerning. The lack of known workarounds means that organizations cannot temporarily mitigate the issue while awaiting patches, forcing them to either upgrade immediately or accept the risk of service disruption.
The fix for this vulnerability was implemented through GitHub commit f3cf67ac5705f4f04721d15e485e192bb319feed, which properly validates input parameters before processing them in the quantization gradient computation. This patch addresses the root cause by ensuring that nonscalar inputs are rejected gracefully rather than causing system termination. Organizations should prioritize upgrading to TensorFlow 2.10.0 where this fix is included, or apply the cherry-picked patches to their supported older versions. The vulnerability demonstrates the importance of proper input validation in machine learning frameworks, as highlighted by ATT&CK technique T1499.004 for avoiding or evading defenses through service interruption. This issue underscores the need for robust error handling in AI infrastructure components and reinforces the critical nature of maintaining secure software supply chains in machine learning environments where system reliability directly impacts business operations and user experience.