{"containers": {"cna": {"affected": [{"product": "tensorflow", "vendor": "tensorflow", "versions": [{"status": "affected", "version": "< 2.1.4"}, {"status": "affected", "version": ">= 2.2.0, < 2.2.3"}, {"status": "affected", "version": ">= 2.3.0, < 2.3.3"}, {"status": "affected", "version": ">= 2.4.0, < 2.4.2"}]}], "descriptions": [{"lang": "en", "value": "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. 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