CVE-2023-25665 in TensorFlow
Summary
by MITRE • 03/25/2023
TensorFlow is an open source platform for machine learning. Prior to versions 2.12.0 and 2.11.1, when `SparseSparseMaximum` is given invalid sparse tensors as inputs, it can give a null pointer error. A fix is included in TensorFlow version 2.12 and version 2.11.1.
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Analysis
by VulDB Data Team • 03/25/2023
The vulnerability identified as CVE-2023-25665 affects the TensorFlow machine learning platform, specifically impacting versions prior to 2.12.0 and 2.11.1. This issue resides within the `SparseSparseMaximum` operation which is designed to compute the element-wise maximum of two sparse tensors. The flaw manifests when the operation receives malformed or invalid sparse tensor inputs, leading to a null pointer dereference condition that can cause application crashes or unexpected behavior. Such vulnerabilities in machine learning frameworks are particularly concerning as they can disrupt critical AI workloads and potentially provide attack vectors for malicious actors seeking to exploit system instability.
The technical implementation of this vulnerability stems from inadequate input validation within the `SparseSparseMaximum` function. When processing sparse tensor data structures, the system fails to properly validate the integrity of the input tensors before attempting operations on them. This lack of proper validation allows malformed tensor representations to propagate through the computation pipeline, ultimately resulting in a null pointer access when the system attempts to reference memory locations that have not been properly initialized. The vulnerability is classified under CWE-476 as a null pointer dereference, which represents a fundamental programming error where a null pointer is used in a context requiring a valid object reference. This type of error can lead to denial of service conditions and potentially provide opportunities for further exploitation if the system is not properly hardened against such conditions.
The operational impact of CVE-2023-25665 extends beyond simple application crashes, as it can severely disrupt machine learning workflows that rely on TensorFlow's sparse tensor operations. In production environments, this vulnerability could cause training jobs to fail unexpectedly, leading to significant downtime and potential loss of computational resources. The vulnerability is particularly dangerous in automated systems where sparse tensor operations are frequently used in neural network computations, as it could enable attackers to cause service disruption through carefully crafted inputs. From an attack perspective, this vulnerability aligns with ATT&CK technique T1499.004 which covers network denial of service attacks, though it specifically targets application-level instability rather than network-level disruption. Organizations using TensorFlow in production environments face increased risk of operational disruption, particularly in scenarios where machine learning pipelines process untrusted data inputs.
The remediation for this vulnerability requires upgrading to TensorFlow versions 2.12.0 or 2.11.1, which include the necessary code fixes to properly validate sparse tensor inputs before processing. System administrators should prioritize this upgrade across all environments where TensorFlow is deployed, particularly in production systems handling critical machine learning workloads. Additionally, implementing proper input validation at the application level can provide defense-in-depth measures against similar issues in other components. Organizations should also consider implementing monitoring solutions to detect anomalous behavior patterns that might indicate exploitation attempts. The fix implemented in the newer versions demonstrates proper error handling practices that ensure invalid inputs are rejected gracefully rather than allowing the system to attempt operations on malformed data structures. This vulnerability underscores the importance of robust input validation in mathematical computing libraries, where improper handling of edge cases can lead to system instability and potential security implications.