https://cve.radiocsirt.org Contains only the most 10 recent entries. http://www.rssboard.org/rss-specification python-feedgen en Fri, 09 Oct 2026 10:51:04 +0000 https://cve.radiocsirt.org/vuln/cve-2021-29549 <p><strong>Affected:</strong> tensorflow</p> <p>TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since `vector_num_elements` is determined based on input shapes(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0. 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. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since `vector_num_elements` is determined based on input shapes(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity is 0. 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-2021-29549 https://cve.radiocsirt.org/vuln/ghsa-x83m-p7pv-ch8v <p><strong>Affected:</strong> PyPI: tensorflow, PyPI: tensorflow-cpu, PyPI: tensorflow-gpu</p> <p>### Impact An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedAdd`:</p> <p>```python import tensorflow as tf</p> <p>x = tf.constant([68, 228], shape=[2, 1], dtype=tf.quint8) y = tf.constant([], shape=[2, 0], dtype=tf.quint8)</p> <p>min_x = tf.constant(10.723421015884028) max_x = tf.constant(15.19578006631113) min_y = tf.constant(-5.539003866682977) max_y = tf.constant(42.18819949559947)</p> <p>tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y) ```</p> <p>This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero.</p> <p>```cc void VectorTensorAddition(const T* vector_data, float min_vector, float max_vector, int64 vector_num_elements, const T* tensor_data, float min_tensor, float max_tensor, int64 tensor_num_elements, float output_min, float output_max, Toutput* output) { for (int i = 0; i &lt; tensor_num_elements; ++i) { const int64 vector_i = i % vector_num_elements; ... } } ```</p> <p>Since `vector_num_elements` is [determined based on input shapes](https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where th…</p> <p><strong>Affected:</strong> PyPI: tensorflow, PyPI: tensorflow-cpu, PyPI: tensorflow-gpu</p> <p>### Impact An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedAdd`:</p> <p>```python import tensorflow as tf</p> <p>x = tf.constant([68, 228], shape=[2, 1], dtype=tf.quint8) y = tf.constant([], shape=[2, 0], dtype=tf.quint8)</p> <p>min_x = tf.constant(10.723421015884028) max_x = tf.constant(15.19578006631113) min_y = tf.constant(-5.539003866682977) max_y = tf.constant(42.18819949559947)</p> <p>tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y) ```</p> <p>This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero.</p> <p>```cc void VectorTensorAddition(const T* vector_data, float min_vector, float max_vector, int64 vector_num_elements, const T* tensor_data, float min_tensor, float max_tensor, int64 tensor_num_elements, float output_min, float output_max, Toutput* output) { for (int i = 0; i &lt; tensor_num_elements; ++i) { const int64 vector_i = i % vector_num_elements; ... } } ```</p> <p>Since `vector_num_elements` is [determined based on input shapes](https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where th…</p> https://cve.radiocsirt.org/vuln/ghsa-x83m-p7pv-ch8v