CVE-2022-29201 in TensorFlow
Summary
by MITRE • 05/21/2022
TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.QuantizedConv2D` does not fully validate the input arguments. In this case, references get bound to `nullptr` for each argument that is empty. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.
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Analysis
by VulDB Data Team • 05/27/2022
The vulnerability identified as CVE-2022-29201 affects TensorFlow's implementation of the `tf.raw_ops.QuantizedConv2D` operation, which is a critical component in machine learning workflows involving quantized neural networks. This flaw exists in TensorFlow versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4, creating a potential security risk that could be exploited in environments where TensorFlow is used for model inference and training. The issue specifically relates to insufficient input validation within the quantized convolution operation, which is commonly used in mobile and edge deployments where computational efficiency is paramount. The vulnerability manifests when empty arguments are passed to the QuantizedConv2D operation, causing references to become bound to null pointers, which can lead to unpredictable behavior and potential system instability.
The technical implementation flaw stems from the lack of proper argument validation within the raw operations layer of TensorFlow. When the `tf.raw_ops.QuantizedConv2D` function receives empty or malformed input parameters, the internal code fails to properly handle these cases, resulting in null pointer references that can cause crashes or memory access violations. This type of vulnerability falls under CWE-476, Null Pointer Dereference, and represents a classic case of inadequate input sanitization in low-level operations. The raw operations interface in TensorFlow is designed for high-performance execution and direct access to underlying computational kernels, but this convenience comes with increased responsibility for proper parameter validation. The flaw demonstrates how optimizations for performance can sometimes compromise security if proper defensive programming practices are not applied.
The operational impact of this vulnerability extends beyond simple application crashes, potentially affecting machine learning deployment environments where TensorFlow is integrated into production systems. In scenarios involving automated model serving or real-time inference pipelines, a null pointer dereference could cause service interruptions or create opportunities for denial-of-service attacks that might be exploited by malicious actors. The vulnerability affects both CPU and GPU execution paths since the issue occurs at the operation level rather than being hardware-specific. Organizations using TensorFlow for production machine learning workloads, particularly those deploying models in mobile applications or edge computing environments, face increased risk of system instability and potential data processing failures. The vulnerability is particularly concerning in regulated environments where system reliability and predictable behavior are required for compliance with industry standards and security frameworks.
Mitigation strategies for CVE-2022-29201 should prioritize immediate version upgrades to TensorFlow 2.9.0, 2.8.1, 2.7.2, or 2.6.4, as these releases contain the necessary patches to address the input validation issues. Organizations should also implement comprehensive testing procedures that validate all input parameters to quantized operations before deployment, particularly in production environments where the vulnerability could be exploited through crafted inputs. Additional defensive measures include implementing proper error handling and logging mechanisms around quantized operations to detect and respond to malformed inputs, as well as conducting regular security assessments of machine learning pipelines to identify similar validation gaps in other operations. From an ATT&CK perspective, this vulnerability could be categorized under T1499.004 - Endpoint Denial of Service, as it represents a potential vector for service disruption in machine learning environments. The patch implementation addresses the root cause by ensuring that all input arguments are properly validated before processing, thereby preventing the null pointer binding that previously occurred during empty argument handling.