CVE-2021-29529 Information

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.ccL62-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.ccL245-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.

CVSS Vector

CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Reference

https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7 https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jfp7-4j67-8r3q

Attack Complexity

LOW

Privileges Required

LOW

User Interaction Required

LOW

Scope

NONE

Confidentiality Impact

UNCHANGED

Integrity Impact

HIGH

Availability Impact

HIGH

Base Score

HIGH

Base Severity

7.8