{"resultsPerPage":1,"startIndex":0,"totalResults":1,"format":"NVD_CVE","version":"2.0","timestamp":"2026-04-16T19:22:04.926","vulnerabilities":[{"cve":{"id":"CVE-2021-29612","sourceIdentifier":"security-advisories@github.com","published":"2021-05-14T20:15:15.990","lastModified":"2024-11-21T06:01:29.567","vulnStatus":"Modified","cveTags":[],"descriptions":[{"lang":"en","value":"TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`. The implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty. Furthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen in this op's implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit 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;Un atacante puede desencadenar un desbordamiento del búfer en una implementación de Eigen de \"tf.raw_ops.BandedTriangularSolve\".&#xa0;La implementación (https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) llama a \"ValidateInputTensors\" no están vacías.&#xa0;Además, dado que la macro \"OP_REQUIRES\" solo detiene una ejecución de la función actual después de configurar la función \"ctx-)status()\" a un valor que no es OK, los llamadores de funciones auxiliares que usan \"OP_REQUIRES\" deben comprobar el valor de \"ctx-)status()\" antes de continuar.&#xa0;Esto no sucede en una implementación de esta operación (https://github.&#xa0;com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), por lo tanto, Una comprobación que está presente tampoco es efectiva.&#xa0;La corrección será incluída en TensorFlow versión 2.5.0.&#xa0;También seleccionaremos este commit en TensorFlow versión 2.4.2, TensorFlow versión 2.3.3, TensorFlow versión 2.2.3 y TensorFlow versión 2.1.4, ya que estos también están afectados y aún están en el rango 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