Division by zero in TFLite

Impact The implementation of fully connected layers in TFLite is vulnerable to a division by zero error: cc const int batchsize = inputsize / filter-dims-data1; An attacker can craft a model such that filter-dims-data1 is 0. Patches We have patched the issue in GitHub commit...

Heap buffer overflow caused by rounding

Impact An attacker can trigger a heap buffer overflow in tf.rawops.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...