{"resultsPerPage":1,"startIndex":0,"totalResults":1,"format":"NVD_CVE","version":"2.0","timestamp":"2026-04-17T10:39:07.383","vulnerabilities":[{"cve":{"id":"CVE-2021-29608","sourceIdentifier":"security-advisories@github.com","published":"2021-05-14T20:15:15.803","lastModified":"2024-11-21T06:01:29.043","vulnStatus":"Modified","cveTags":[],"descriptions":[{"lang":"en","value":"TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple `DCHECK` validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range."},{"lang":"es","value":"TensorFlow es una plataforma de código abierto de extremo a extremo para el aprendizaje automático.&#xa0;Debido a una falta de comprobación en \"tf.raw_ops.RaggedTensorToTensor\", un atacante puede explotar un comportamiento indefinido si los argumentos de entrada están vacíos.&#xa0;La implementación (https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) solo comprueba que uno de los tensores no esté vacío, sino que no comprueba si está vacío, otros.&#xa0;Se presentan múltiples comprobaciones de \"DCHECK\" para evitar la pila de OOB, pero estas no son operativas en unas versiones de lanzamiento, por lo que no evitan nada.&#xa0;La corrección será incluida en TensorFlow versión 2.5.0.&#xa0;También seleccionaremos este commits en TensorFlow versión 2.4.2, TensorFlow versión 2.3.3, TensorFlow versión 2.2.3 y TensorFlow versión 2.1.4, ya que también están afectadas y aún se encuentran en el rango 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