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GHSA-cpf4-wx82-gxp6 BIT-tensorflow-2021-41222 CVE-2021-41222 PYSEC-2021-414 PYSEC-2021-631 PYSEC-2021-829

Segfault due to negative splits in `SplitV`

Medium PyPI
Severity
0.0 / 10
Medium · CVSS 4.0
Exploit chance (EPSS)
0.19%
Chance of exploitation in the next 30 days. Higher than 8.39% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-682 Incorrect Calculation
Fix
Available
3 packages have a fixed version

Description

Impact

The implementation of SplitV can trigger a segfault is an attacker supplies negative arguments:

import tensorflow as tf

tf.raw_ops.SplitV(
  value=tf.constant([]),
  size_splits=[-1, -2]
  ,axis=0,
  num_split=2)

This occurs whenever size_splits contains more than one value and at least one value is negative.

Patches

We have patched the issue in GitHub commit 25d622ffc432acc736b14ca3904177579e733cc6.

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.

Affected versions

PyPI

PackageVulnerableFixed in
tensorflow
>= 2.6.0, < 2.6.1
2.6.1
tensorflow
>= 2.5.0, < 2.5.2
2.5.2
tensorflow
< 2.4.4
2.4.4
tensorflow
< 2.4.4
>= 2.5.0, < 2.5.2
>= 2.6.0, < 2.6.1
>= 2.7.0rc0, < 2.7.0
2.4.4
2.5.2
2.6.1
2.7.0
tensorflow-cpu
>= 2.6.0, < 2.6.1
2.6.1
tensorflow-cpu
>= 2.5.0, < 2.5.2
2.5.2
tensorflow-cpu
< 2.4.4
2.4.4
tensorflow-cpu
< 2.4.4
>= 2.5.0, < 2.5.2
>= 2.6.0, < 2.6.1
>= 2.7.0rc0, < 2.7.0
2.4.4
2.5.2
2.6.1
2.7.0
tensorflow-gpu
>= 2.6.0, < 2.6.1
2.6.1
tensorflow-gpu
>= 2.5.0, < 2.5.2
2.5.2
tensorflow-gpu
< 2.4.4
2.4.4
tensorflow-gpu
< 2.4.4
>= 2.5.0, < 2.5.2
>= 2.6.0, < 2.6.1
>= 2.7.0rc0, < 2.7.0
2.4.4
2.5.2
2.6.1
2.7.0

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 → 2.6.1

pip
pip install --upgrade "tensorflow>=2.6.1"
poetry
poetry add "tensorflow>=2.6.1"
uv
uv add "tensorflow>=2.6.1"

tensorflow-cpu PyPI → 2.6.1

pip
pip install --upgrade "tensorflow-cpu>=2.6.1"
poetry
poetry add "tensorflow-cpu>=2.6.1"
uv
uv add "tensorflow-cpu>=2.6.1"

tensorflow-gpu PyPI → 2.6.1

pip
pip install --upgrade "tensorflow-gpu>=2.6.1"
poetry
poetry add "tensorflow-gpu>=2.6.1"
uv
uv add "tensorflow-gpu>=2.6.1"

Severity breakdown

CVSS 4.0 0.0

CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

Attack vector
Local
Attack complexity
Low
Attack requirements
None
Privileges required
Low
User interaction
None
Confidentiality (vulnerable system)
None
Integrity (vulnerable system)
None
Availability (vulnerable system)
High
Confidentiality (subsequent systems)
None
Integrity (subsequent systems)
None
Availability (subsequent systems)
None

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-682: Incorrect Calculation

The product performs a calculation that generates incorrect or unintended results that are later used in security-critical decisions or resource management.

How to prevent it
  • Implementation: Understand your programming language's underlying representation and how it interacts with numeric calculation. Pay close attention to byte size discrepancies, precision, signed/unsigned distinctions, truncation, conversion and casting between types, "not-a-number" calculations, and how your language handles numbers that are too large or too small for its underlying representation.
  • Implementation: Perform input validation on any numeric input by ensuring that it is within the expected range. Enforce that the input meets both the minimum and maximum requirements for the expected range.
  • Implementation: Use the appropriate type for the desired action. For example, in C/C++, only use unsigned types for values that could never be negative, such as height, width, or other numbers related to quantity.
  • Architecture and Design: Use languages, libraries, or frameworks that make it easier to handle numbers without unexpected consequences. Examples include safe integer handling packages such as SafeInt (C++) or IntegerLib (C or C++).
  • Architecture and Design: Use languages, libraries, or frameworks that make it easier to handle numbers without unexpected consequences. Examples include safe integer handling packages such as SafeInt (C++) or IntegerLib (C or C++).
  • Implementation: Examine compiler warnings closely and eliminate problems with potential security implications, such as signed / unsigned mismatch in memory operations, or use of uninitialized variables. Even if the weakness is rarely exploitable, a single failure may lead to the compromise of the entire system.

References

Advisories

Packages

Other links

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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