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Advisory Package Curation

CVE-2021-29568

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Advisory Summaries

github_osv/GHSA-4p4p-www8-8fv9

Reference binding to null in `ParameterizedTruncatedNormal` ### Impact An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`: ```python import tensorflow as tf shape = tf.constant([], shape=[0], dtype=tf.int32) means = tf.constant((1), dtype=tf.float32) stdevs = tf.constant((1), dtype=tf.float32) minvals = tf.constant((1), dtype=tf.float32) maxvals = tf.constant((1), dtype=tf.float32) tf.raw_ops.ParameterizedTruncatedNormal( shape=shape, means=means, stdevs=stdevs, minvals=minvals, maxvals=maxvals) ``` This is because the implementation https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630 does not validate input arguments before accessing the first element of `shape`: ```cc int32 num_batches = shape_tensor.flat<int32>()(0); ``` If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. ### Patches We have patched the issue in GitHub commit 5e52ef5a461570cfb68f3bdbbebfe972cb4e0fd8 https://github.com/tensorflow/tensorflow/commit/5e52ef5a461570cfb68f3bdbbebfe972cb4e0fd8. 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. ### For more information Please consult our security guide https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.

gitlab/pypi/tensorflow-cpu/CVE-2021-29568

Reference binding to null in `ParameterizedTruncatedNormal` An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`: ```python import tensorflow as tf shape = tf.constant([], shape=[0], dtype=tf.int32) means = tf.constant((1), dtype=tf.float32) stdevs = tf.constant((1), dtype=tf.float32) minvals = tf.constant((1), dtype=tf.float32) maxvals = tf.constant((1), dtype=tf.float32) tf.raw_ops.ParameterizedTruncatedNormal( shape=shape, means=means, stdevs=stdevs, minvals=minvals, maxvals=maxvals) ```

gitlab/pypi/tensorflow/CVE-2021-29568

Reference binding to null in `ParameterizedTruncatedNormal` An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`: ```python import tensorflow as tf shape = tf.constant([], shape=[0], dtype=tf.int32) means = tf.constant((1), dtype=tf.float32) stdevs = tf.constant((1), dtype=tf.float32) minvals = tf.constant((1), dtype=tf.float32) maxvals = tf.constant((1), dtype=tf.float32) tf.raw_ops.ParameterizedTruncatedNormal( shape=shape, means=means, stdevs=stdevs, minvals=minvals, maxvals=maxvals) ```

gitlab/pypi/tensorflow-gpu/CVE-2021-29568

Reference binding to null in `ParameterizedTruncatedNormal` An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`: ```python import tensorflow as tf shape = tf.constant([], shape=[0], dtype=tf.int32) means = tf.constant((1), dtype=tf.float32) stdevs = tf.constant((1), dtype=tf.float32) minvals = tf.constant((1), dtype=tf.float32) maxvals = tf.constant((1), dtype=tf.float32) tf.raw_ops.ParameterizedTruncatedNormal( shape=shape, means=means, stdevs=stdevs, minvals=minvals, maxvals=maxvals) ```

pypa/tensorflow-cpu/PYSEC-2021-496

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. 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.

pypa/tensorflow-gpu/PYSEC-2021-694

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. 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.

pypa/tensorflow/PYSEC-2021-205

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. 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.

pysec/PYSEC-2021-205

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. 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.

pysec/PYSEC-2021-496

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. 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.

pysec/PYSEC-2021-694

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. 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.