CVE-2022-29202 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.ragged.constant` does not fully validate the input arguments. This results in a denial of service by consuming all available memory. 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-2022-29202 affects TensorFlow, a widely used open source machine learning platform that powers numerous applications across various industries. This issue specifically targets the `tf.ragged.constant` function implementation within TensorFlow's codebase, representing a critical security flaw that impacts multiple versions of the framework. The vulnerability stems from inadequate input validation mechanisms that fail to properly sanitize or verify the arguments provided to this particular function, creating a pathway for malicious actors to exploit the system's memory management capabilities.
The technical flaw manifests in the improper handling of input arguments within the `tf.ragged.constant` function, which is designed to create ragged tensors from nested lists or arrays. When developers or attackers provide malformed or specially crafted input parameters, the function fails to validate these inputs adequately, leading to uncontrolled memory consumption patterns. This validation gap allows an attacker to construct specific input sequences that cause the function to allocate excessive memory resources without proper bounds checking or resource limits. The vulnerability operates at the core level of TensorFlow's tensor manipulation capabilities, making it particularly dangerous as it can be triggered through normal usage patterns of the machine learning framework.
The operational impact of this vulnerability extends beyond simple denial of service scenarios, as it can lead to complete system crashes or resource exhaustion across various computing environments. When exploited, the vulnerability consumes all available memory resources, effectively rendering the system unusable for legitimate machine learning operations and potentially causing cascading failures in larger distributed systems. This type of memory exhaustion attack is particularly concerning in production environments where TensorFlow is used for critical AI workloads, as it can compromise entire data processing pipelines and service availability. The vulnerability affects organizations relying on TensorFlow versions prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4, creating widespread exposure across the machine learning ecosystem.
Organizations should immediately prioritize upgrading their TensorFlow installations to versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4 to remediate this vulnerability, as these releases contain the necessary patches to address the input validation issues. Additionally, implementing proper input sanitization measures at the application level can provide an additional layer of protection, though this approach is less reliable than the official patches. System administrators should also consider monitoring memory usage patterns and implementing automated alerts for unusual resource consumption that could indicate exploitation attempts. The vulnerability aligns with CWE-129, which addresses insufficient input validation, and represents a potential vector for attacks classified under the MITRE ATT&CK framework's privilege escalation and denial of service categories, particularly when targeting machine learning infrastructure and data processing systems.