title: Heap buffer overflow caused by rounding
description: 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.
problem types: CWE-131: Incorrect Calculation of Buffer Size
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
}
metrics:
cvssV3_1: [
attackComplexity: HIGH
attackVector: LOCAL
availabilityImpact: LOW
baseScore: 2.5
baseSeverity: LOW
confidentialityImpact: NONE
integrityImpact: NONE
privilegesRequired: LOW
scope: UNCHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
version: 3.1
]