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GHSA-54ch-gjq5-4976 BIT-tensorflow-2022-29205 CVE-2022-29205 PYSEC-2026-3104 PYSEC-2026-3274 PYSEC-2026-950

Segfault due to missing support for quantized types

Medium PyPI
Severity
5.5 / 10
Medium · CVSS 3.1
Exploit chance (EPSS)
0.33%
Chance of exploitation in the next 30 days. Higher than 25.60% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-476 NULL Pointer Dereference
CWE-908 Use of Uninitialized Resource
Fix
Available
3 packages have a fixed version

Description

Impact

There is a potential for segfault / denial of service in TensorFlow by calling tf.compat.v1.* ops which don't yet have support for quantized types (added after migration to TF 2.x):

import numpy as np
import tensorflow as tf

tf.compat.v1.placeholder_with_default(input=np.array([2]),shape=tf.constant(dtype=tf.qint8, value=np.array([1])))

In these scenarios, since the kernel is missing, a nullptr value is passed to ParseDimensionValue for the py_value argument. Then, this is dereferenced, resulting in segfault.

Patches

We have patched the issue in GitHub commit 237822b59fc504dda2c564787f5d3ad9c4aa62d9.

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, 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 Hong Jin from Singapore Management University.

Affected versions

PyPI

PackageVulnerableFixed in
tensorflow
< 2.6.4
2.6.4
tensorflow
>= 2.7.0, < 2.7.2
2.7.2
tensorflow
>= 2.8.0, < 2.8.1
2.8.1
tensorflow
< 2.6.4
>= 2.7.0, < 2.7.2
>= 2.8.0, < 2.8.1
2.6.4
2.7.2
2.8.1
tensorflow-cpu
< 2.6.4
2.6.4
tensorflow-cpu
>= 2.7.0, < 2.7.2
2.7.2
tensorflow-cpu
>= 2.8.0, < 2.8.1
2.8.1
tensorflow-cpu
< 2.6.4
>= 2.7.0, < 2.7.2
>= 2.8.0, < 2.8.1
2.6.4
2.7.2
2.8.1
tensorflow-gpu
< 2.6.4
2.6.4
tensorflow-gpu
>= 2.7.0, < 2.7.2
2.7.2
tensorflow-gpu
>= 2.8.0, < 2.8.1
2.8.1
tensorflow-gpu
< 2.6.4
>= 2.7.0, < 2.7.2
>= 2.8.0, < 2.8.1
2.6.4
2.7.2
2.8.1

How to fix

Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.

tensorflow PyPI → 2.6.4

pip
pip install --upgrade "tensorflow>=2.6.4"
poetry
poetry add "tensorflow>=2.6.4"
uv
uv add "tensorflow>=2.6.4"

tensorflow-cpu PyPI → 2.6.4

pip
pip install --upgrade "tensorflow-cpu>=2.6.4"
poetry
poetry add "tensorflow-cpu>=2.6.4"
uv
uv add "tensorflow-cpu>=2.6.4"

tensorflow-gpu PyPI → 2.6.4

pip
pip install --upgrade "tensorflow-gpu>=2.6.4"
poetry
poetry add "tensorflow-gpu>=2.6.4"
uv
uv add "tensorflow-gpu>=2.6.4"

Severity breakdown

CVSS 3.1 5.5

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

Attack vector
Local
Attack complexity
Low
Privileges required
Low
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

Weakness

CWE-476: NULL Pointer Dereference

The product dereferences a pointer that it expects to be valid but is NULL.

How to prevent it
  • Implementation: For any pointers that could have been modified or provided from a function that can return NULL, check the pointer for NULL before use. When working with a multithreaded or otherwise asynchronous environment, ensure that proper locking APIs are used to lock before the check, and unlock when it has finished [REF-1484].
  • Requirements: Select a programming language that is not susceptible to these issues.
  • Implementation: Check the results of all functions that return a value and verify that the value is non-null before acting upon it.
  • Architecture and Design: Identify all variables and data stores that receive information from external sources, and apply input validation to make sure that they are only initialized to expected values.
  • Implementation: Explicitly initialize all variables and other data stores, either during declaration or just before the first usage.

CWE-908: Use of Uninitialized Resource

The product uses or accesses a resource that has not been initialized.

How to prevent it
  • Implementation: Explicitly initialize the resource before use. If this is performed through an API function or standard procedure, follow all required steps.
  • Implementation: Pay close attention to complex conditionals that affect initialization, since some branches might not perform the initialization.
  • Implementation: Avoid race conditions (CWE-362) during initialization routines.
  • Build and Compilation: Run or compile the product with settings that generate warnings about uninitialized variables or data.

References

Timeline

  1. Published
  2. Last updated by the source
  3. Latest EPSS score
AdvisorySeverityWhy it's related
GHSA-gjh7-xx4r-x345
TensorFlow has segfault in array_ops.upper_bound
High Also affects tensorflow
GHSA-fxgc-95xx-grvq
TensorFlow Denial of Service vulnerability
Medium Also affects tensorflow
GHSA-93vr-9q9m-pj8p
TensorFlow vulnerable to Out-of-Bounds Read in DynamicStitch
High Also affects tensorflow
GHSA-qjqc-vqcf-5qvj
TensorFlow vulnerable to seg fault in `tf.raw_ops.Print`
High Also affects tensorflow
GHSA-7jvm-xxmr-v5cw
TensorFlow vulnerable to integer overflow in EditDistance
High Also affects tensorflow
GHSA-64jg-wjww-7c5w
TensorFlow has Null Pointer Error in TensorArrayConcatV2
High Also affects tensorflow
GHSA-6hg6-5c2q-7rcr
TensorFlow has Heap-buffer-overflow in AvgPoolGrad
High Also affects tensorflow
GHSA-558h-mq8x-7q9g
TensorFlow has Null Pointer Error in SparseSparseMaximum
High Also affects tensorflow
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