2.5 Low
CVSS3
Attack Vector
LOCAL
Attack Complexity
HIGH
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED
Confidentiality Impact
NONE
Integrity Impact
NONE
Availability Impact
LOW
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
0.0005 Low
EPSS
Percentile
17.8%
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.
[
{
"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"
}
]
}
]