CVE-2022-36026 in TensorFlow
Summary
by MITRE • 09/17/2022
TensorFlow is an open source platform for machine learning. If `QuantizeAndDequantizeV3` is given a nonscalar `num_bits` input tensor, it results in a `CHECK` fail that can be used to trigger a denial of service attack. We have patched the issue in GitHub commit f3f9cb38ecfe5a8a703f2c4a8fead434ef291713. 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.
VulDB is the best source for vulnerability data and more expert information about this specific topic.
Analysis
by VulDB Data Team • 10/20/2022
The vulnerability identified as CVE-2022-36026 affects TensorFlow, a widely-used open source machine learning platform that serves as the foundation for numerous artificial intelligence applications across various industries. This issue specifically targets the QuantizeAndDequantizeV3 operation within TensorFlow's computational graph processing framework, where the system fails to properly validate input parameters. The flaw manifests when the operation receives a nonscalar tensor as the `num_bits` parameter, which should typically be a scalar value representing the number of bits for quantization. This improper input handling creates a critical check failure that can be exploited by malicious actors to disrupt service availability.
The technical implementation of this vulnerability stems from inadequate input validation within the QuantizeAndDequantizeV3 function, which relies on a CHECK macro that performs runtime assertions to verify parameter correctness. When a nonscalar tensor is passed as the `num_bits` argument, the system's internal validation logic fails to properly handle this edge case, resulting in an abrupt termination of the process. This behavior aligns with CWE-617, which addresses reachable assertions, and represents a classic denial of service vulnerability where an attacker can cause the application to crash through malformed input. The vulnerability operates at the software level within TensorFlow's core computation engine, specifically affecting the graph execution phase where quantization operations are processed.
The operational impact of this vulnerability extends beyond simple service disruption, as it can be leveraged to create significant availability issues in machine learning deployment environments. Systems utilizing TensorFlow for inference or training tasks that process untrusted input data may become vulnerable to denial of service attacks, potentially affecting production environments, cloud services, or edge computing deployments. The vulnerability affects multiple TensorFlow versions including 2.7.2, 2.8.1, 2.9.1, and the upcoming 2.10.0 release, indicating the widespread nature of the issue within the supported version lifecycle. Organizations using TensorFlow in critical applications such as autonomous vehicles, financial services, healthcare diagnostics, or industrial automation systems face potential operational risks that could compromise system reliability and business continuity.
The security community has addressed this vulnerability through a targeted fix implemented in GitHub commit f3f9cb38ecfe5a8a703f2c4a8fead434ef291713, which introduces proper input validation for the `num_bits` parameter in the QuantizeAndDequantizeV3 operation. This fix ensures that the system properly rejects nonscalar inputs rather than allowing them to trigger the CHECK failure mechanism. The patch has been integrated into TensorFlow 2.10.0 and backported to older supported versions to maintain security coverage across the affected release cycle. Organizations should prioritize applying these updates as they become available, since no effective workarounds exist for this particular vulnerability. The mitigation strategy aligns with ATT&CK technique T1499.004, which covers network denial of service attacks, and demonstrates the importance of proper input validation in preventing exploitation of software flaws that can lead to service disruption. Given the critical nature of this vulnerability and its potential impact on machine learning infrastructure, system administrators should implement immediate patch management procedures to ensure all affected TensorFlow installations are properly secured.