https://cve.radiocsirt.org Contains only the most 10 recent entries. http://www.rssboard.org/rss-specification python-feedgen en Sun, 18 Oct 2026 02:12:12 +0000 https://cve.radiocsirt.org/vuln/cve-2021-29571 <p><strong>Affected:</strong> tensorflow</p> <p>TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices. 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.</p> <p><strong>Affected:</strong> tensorflow</p> <p>TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices. 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.</p> https://cve.radiocsirt.org/vuln/cve-2021-29571 https://cve.radiocsirt.org/vuln/ghsa-whr9-vfh2-7hm6 <p><strong>Affected:</strong> PyPI: tensorflow, PyPI: tensorflow-cpu, PyPI: tensorflow-gpu</p> <p>### Impact The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:</p> <p>```python import tensorflow as tf</p> <p>images = tf.fill([10, 96, 0, 1], 0.) boxes = tf.fill([10, 53, 0], 0.) colors = tf.fill([0, 1], 0.)</p> <p>tf.raw_ops.DrawBoundingBoxesV2(images=images, boxes=boxes, colors=colors) ```</p> <p>The [implementation](https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption:</p> <p>```cc const auto tboxes = boxes.tensor&lt;T, 3&gt;(); for (int64 bb = 0; bb &lt; num_boxes; ++bb) { ... const int64 min_box_row = static_cast&lt;float&gt;(tboxes(b, bb, 0)) * (height - 1); const int64 max_box_row = static_cast&lt;float&gt;(tboxes(b, bb, 2)) * (height - 1); const int64 min_box_col = static_cast&lt;float&gt;(tboxes(b, bb, 1)) * (width - 1); const int64 max_box_col = static_cast&lt;float&gt;(tboxes(b, bb, 3)) * (width - 1); ... } ```</p> <p>If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices.</p> <p>### Patche…</p> <p><strong>Affected:</strong> PyPI: tensorflow, PyPI: tensorflow-cpu, PyPI: tensorflow-gpu</p> <p>### Impact The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:</p> <p>```python import tensorflow as tf</p> <p>images = tf.fill([10, 96, 0, 1], 0.) boxes = tf.fill([10, 53, 0], 0.) colors = tf.fill([0, 1], 0.)</p> <p>tf.raw_ops.DrawBoundingBoxesV2(images=images, boxes=boxes, colors=colors) ```</p> <p>The [implementation](https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption:</p> <p>```cc const auto tboxes = boxes.tensor&lt;T, 3&gt;(); for (int64 bb = 0; bb &lt; num_boxes; ++bb) { ... const int64 min_box_row = static_cast&lt;float&gt;(tboxes(b, bb, 0)) * (height - 1); const int64 max_box_row = static_cast&lt;float&gt;(tboxes(b, bb, 2)) * (height - 1); const int64 min_box_col = static_cast&lt;float&gt;(tboxes(b, bb, 1)) * (width - 1); const int64 max_box_col = static_cast&lt;float&gt;(tboxes(b, bb, 3)) * (width - 1); ... } ```</p> <p>If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices.</p> <p>### Patche…</p> https://cve.radiocsirt.org/vuln/ghsa-whr9-vfh2-7hm6