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    <item>
      <title>CVE-2021-29558 — Heap buffer overflow in `SparseSplit`</title>
      <link>https://cve.radiocsirt.org/vuln/cve-2021-29558</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; tensorflow&lt;/p&gt;
&lt;p&gt;TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `tf.raw_ops.SparseSplit`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/699bff5d961f0abfde8fa3f876e6d241681fbef8/tensorflow/core/util/sparse/sparse_tensor.h#L528-L530) accesses an array element based on a user controlled offset. 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.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; tensorflow&lt;/p&gt;
&lt;p&gt;TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `tf.raw_ops.SparseSplit`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/699bff5d961f0abfde8fa3f876e6d241681fbef8/tensorflow/core/util/sparse/sparse_tensor.h#L528-L530) accesses an array element based on a user controlled offset. 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.&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://cve.radiocsirt.org/vuln/cve-2021-29558</guid>
    </item>
    <item>
      <title>GHSA-mqh2-9wrp-vx84 — Heap buffer overflow in `SparseSplit`</title>
      <link>https://cve.radiocsirt.org/vuln/ghsa-mqh2-9wrp-vx84</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: tensorflow, PyPI: tensorflow-cpu, PyPI: tensorflow-gpu&lt;/p&gt;
&lt;p&gt;### Impact
An attacker can cause a heap buffer overflow in `tf.raw_ops.SparseSplit`:&lt;/p&gt;
&lt;p&gt;```python
import tensorflow as tf&lt;/p&gt;
&lt;p&gt;shape_dims = tf.constant(0, dtype=tf.int64)
indices = tf.ones([1, 1], dtype=tf.int64)
values = tf.ones([1], dtype=tf.int64)
shape = tf.ones([1], dtype=tf.int64)&lt;/p&gt;
&lt;p&gt;tf.raw_ops.SparseSplit(
    split_dim=shape_dims, indices=indices, values=values,
    shape=shape, num_split=1)
```&lt;/p&gt;
&lt;p&gt;This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/699bff5d961f0abfde8fa3f876e6d241681fbef8/tensorflow/core/util/sparse/sparse_tensor.h#L528-L530) accesses an array element based on a user controlled offset:&lt;/p&gt;
&lt;p&gt;```cc
const int dim = input_tensor.indices().matrix&amp;lt;int64&amp;gt;()(i, split_dim);
int slice_index = GetSliceIndex(dim, split_size, residual);
num_values[slice_index]++;
```&lt;/p&gt;
&lt;p&gt;This results in overriding values on the heap.&lt;/p&gt;
&lt;p&gt;### Patches
We have patched the issue in GitHub commit [8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31](https://github.com/tensorflow/tensorflow/commit/8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31).&lt;/p&gt;
&lt;p&gt;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.&lt;/p&gt;
&lt;p&gt;### 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.&lt;/p&gt;
&lt;p&gt;### Att…&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: tensorflow, PyPI: tensorflow-cpu, PyPI: tensorflow-gpu&lt;/p&gt;
&lt;p&gt;### Impact
An attacker can cause a heap buffer overflow in `tf.raw_ops.SparseSplit`:&lt;/p&gt;
&lt;p&gt;```python
import tensorflow as tf&lt;/p&gt;
&lt;p&gt;shape_dims = tf.constant(0, dtype=tf.int64)
indices = tf.ones([1, 1], dtype=tf.int64)
values = tf.ones([1], dtype=tf.int64)
shape = tf.ones([1], dtype=tf.int64)&lt;/p&gt;
&lt;p&gt;tf.raw_ops.SparseSplit(
    split_dim=shape_dims, indices=indices, values=values,
    shape=shape, num_split=1)
```&lt;/p&gt;
&lt;p&gt;This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/699bff5d961f0abfde8fa3f876e6d241681fbef8/tensorflow/core/util/sparse/sparse_tensor.h#L528-L530) accesses an array element based on a user controlled offset:&lt;/p&gt;
&lt;p&gt;```cc
const int dim = input_tensor.indices().matrix&amp;lt;int64&amp;gt;()(i, split_dim);
int slice_index = GetSliceIndex(dim, split_size, residual);
num_values[slice_index]++;
```&lt;/p&gt;
&lt;p&gt;This results in overriding values on the heap.&lt;/p&gt;
&lt;p&gt;### Patches
We have patched the issue in GitHub commit [8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31](https://github.com/tensorflow/tensorflow/commit/8ba6fa29cd8bf9cef9b718dc31c78c73081f5b31).&lt;/p&gt;
&lt;p&gt;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.&lt;/p&gt;
&lt;p&gt;### 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.&lt;/p&gt;
&lt;p&gt;### Att…&lt;/p&gt;</content:encoded>
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