https://cve.radiocsirt.org/rss/recent/all/10 Most recent entries from all 2026-10-09T19:04:50.911135+00:00 Vulnerability-Lookup csirt@opendfir.org python-feedgen Contains only the most 10 recent entries. https://cve.radiocsirt.org/vuln/cve-2021-29529 CVE-2021-29529 — Heap buffer overflow caused by rounding 2026-10-09T19:04:50.941815+00:00

Affected: tensorflow

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value. For some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, in the interpolation code(https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), this would result in heap buffer overflow. 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.

 https://cve.radiocsirt.org/vuln/ghsa-jfp7-4j67-8r3q GHSA-jfp7-4j67-8r3q — Heap buffer overflow caused by rounding 2026-10-09T19:04:50.941883+00:00

Affected: PyPI: tensorflow, PyPI: tensorflow-cpu, PyPI: tensorflow-gpu

### Impact An attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements:

```python import tensorflow as tf

l = [256, 328, 361, 17, 361, 361, 361, 361, 361, 361, 361, 361, 361, 361, 384] images = tf.constant(l, shape=[1, 1, 15, 1], dtype=tf.qint32) size = tf.constant([12, 6], shape=[2], dtype=tf.int32) min = tf.constant(80.22522735595703) max = tf.constant(80.39215850830078)

tf.raw_ops.QuantizedResizeBilinear(images=images, size=size, min=min, max=max, align_corners=True, half_pixel_centers=True) ```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value:

```cc const float in_f = std::floor(in); interpolation->lower[i] = std::max(static_cast<int64>(in_f), static_cast<int64>(0)); interpolation->upper[i] = std::min(static_cast<int64>(std::ceil(in)), in_size - 1); ``` For some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, [in the interpolation code](https:/…