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BIT-tensorflow-2021-29535 — Heap buffer overflow in `QuantizedMul`

Affected: Bitnami: tensorflow

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedMul` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/87cf4d3ea9949051e50ca3f071fc909538a51cd0/tensorflow/core/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in 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.

cnvd-2021-36543

EUVD-2026-26422

fkie_cve-2021-29535

GHSA-m3f9-w3p3-p669 — Heap buffer overflow in `QuantizedMul`

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

### Impact An attacker can cause a heap buffer overflow in `QuantizedMul` by passing in invalid thresholds for the quantization:

```python import tensorflow as tf

x = tf.constant([256, 328], shape=[1, 2], dtype=tf.quint8) y = tf.constant([256, 328], shape=[1, 2], dtype=tf.quint8) min_x = tf.constant([], dtype=tf.float32) max_x = tf.constant([], dtype=tf.float32) min_y = tf.constant([], dtype=tf.float32) max_y = tf.constant([], dtype=tf.float32)

tf.raw_ops.QuantizedMul(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y) ```

This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/87cf4d3ea9949051e50ca3f071fc909538a51cd0/tensorflow/core/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly:

```cc const float min_x = context->input(2).flat<float>()(0); const float max_x = context->input(3).flat<float>()(0); const float min_y = context->input(4).flat<float>()(0); const float max_y = context->input(5).flat<float>()(0); ```

However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow.

### Patches We have patched the issue in GitHub commit [efea03b38fb8d3b81762237dc85e579cc5fc6e87](https://github.com/tensorflow/tensorflow/commit/efea03b38fb8d3b81762237dc85e579cc5fc6e87).

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, Ten…

gsd-2021-29535

PYSEC-2021-172

Affected: PyPI: tensorflow