CVE-2022-29207 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, multiple TensorFlow operations misbehave in eager mode when the resource handle provided to them is invalid. In graph mode, it would have been impossible to perform these API calls, but migration to TF 2.x eager mode opened up this vulnerability. If the resource handle is empty, then a reference is bound to a null pointer inside TensorFlow codebase (various codepaths). This is undefined behavior. 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
TensorFlow represents one of the most widely adopted machine learning frameworks globally, serving as the foundation for numerous artificial intelligence applications across diverse industries. The vulnerability identified as CVE-2022-29207 specifically targets the framework's behavior in eager execution mode, which became the default execution paradigm in TensorFlow 2.x. This shift from graph mode to eager mode introduced new attack surfaces that were previously mitigated by the static nature of graph execution. The vulnerability stems from improper handling of resource handles within TensorFlow operations, creating conditions where invalid resource references could lead to critical system instability.
The technical flaw manifests when TensorFlow operations process resource handles that are empty or invalid during eager mode execution. This condition creates a scenario where references become bound to null pointers within the TensorFlow codebase, resulting in undefined behavior that can lead to application crashes, memory corruption, or potentially exploitable conditions. The vulnerability is particularly concerning because it affects multiple versions of TensorFlow, with the specific affected versions being those prior to 2.9.0, 2.8.1, 2.7.2, and 2.6.4, indicating a widespread impact across the framework's version history. The issue occurs across various codepaths within TensorFlow's internal implementation, making it difficult to predict and prevent through simple code inspection.
The operational impact of this vulnerability extends beyond simple application instability, potentially compromising the integrity of machine learning workflows and training processes. When resource handles become invalid during eager execution, it can cause cascading failures that disrupt ongoing model training or inference operations, leading to significant downtime and potential data loss. The vulnerability's exploitation could enable attackers to cause denial of service conditions, making TensorFlow-based systems unreliable for critical applications. This risk is particularly acute in production environments where TensorFlow is used for automated machine learning pipelines, as the undefined behavior could be leveraged to disrupt service availability or potentially execute arbitrary code.
The vulnerability aligns with CWE-476, which describes null pointer dereference conditions, and represents a classic example of how modern execution models can introduce new security risks. From an adversarial perspective, this vulnerability maps to ATT&CK technique T1499.004, which involves network disruption through resource exhaustion or invalid resource handling, and T1589.002, which covers exploitation of vulnerabilities in software libraries. Organizations using TensorFlow should prioritize immediate patching of affected versions, with the recommended remediation being migration to TensorFlow versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4, which contain the necessary code fixes. Additionally, implementing proper input validation and resource handle verification within application code can provide additional defense-in-depth measures against potential exploitation of this vulnerability.