CVE-2022-29204 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.UnsortedSegmentJoin` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `num_segments` is a positive scalar but there is no validation. Since this value is used to allocate the output tensor, a negative value would result in a `CHECK`-failure (assertion failure), as per TFSA-2021-198. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Analysis
by VulDB Data Team • 06/25/2025
The vulnerability identified as CVE-2022-29204 affects TensorFlow versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4, specifically within the `tf.raw_ops.UnsortedSegmentJoin` operation implementation. This flaw represents a classic input validation weakness that can be exploited to cause denial of service conditions. The vulnerability stems from insufficient validation of input arguments, particularly the `num_segments` parameter, which is critical for proper tensor allocation and operation execution. According to the TensorFlow Security Advisory TFSA-2021-198, the implementation makes assumptions about input values that are not properly verified, creating a dangerous gap in the security model of the machine learning platform.
The technical flaw manifests when the `num_segments` parameter is not validated as a positive scalar value, despite being essential for determining output tensor allocation size. When a negative value is passed to this parameter, the system encounters a CHECK-failure or assertion failure, which terminates the program execution abruptly. This behavior aligns with CWE-704, which classifies improper input validation as a weakness that can lead to various security issues including denial of service. The vulnerability specifically impacts the raw operations layer of TensorFlow where low-level tensor manipulations occur, making it particularly dangerous for applications that process untrusted input data through machine learning pipelines. The flaw demonstrates a fundamental lack of defensive programming practices in the tensor operation implementation.
The operational impact of this vulnerability extends beyond simple denial of service, as it can be leveraged by attackers to disrupt machine learning services and applications that rely on TensorFlow. When exploited, the CHECK-failure causes immediate program termination, potentially affecting production systems, training pipelines, or inference services that process user-provided data. The vulnerability is particularly concerning in cloud environments where TensorFlow-based services might be exposed to untrusted inputs, as attackers could systematically send malicious `num_segments` values to crash services repeatedly. This type of attack pattern aligns with ATT&CK technique T1499.004, which covers network denial of service attacks, and represents a form of resource exhaustion through program termination rather than memory consumption.
Mitigation strategies for this vulnerability require immediate upgrade to patched TensorFlow versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4, where proper input validation has been implemented for the `num_segments` parameter. Organizations should also implement input sanitization measures at application layers that consume TensorFlow operations, ensuring that all parameters passed to raw operations are validated before execution. Additional defensive measures include monitoring for unusual program termination patterns and implementing proper error handling that prevents cascading failures in machine learning service architectures. The vulnerability serves as a reminder of the importance of comprehensive input validation in security-critical systems and demonstrates how seemingly minor implementation oversights can create significant operational risks in complex software platforms.