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GHSA-hhvc-g5hv-48c6 BIT-tensorflow-2020-26268 CVE-2020-26268 PYSEC-2020-255 PYSEC-2020-299 PYSEC-2020-334

Write to immutable memory region in TensorFlow

Medium PyPI
Severity
0.0 / 10
Medium · CVSS 4.0
Exploit chance (EPSS)
0.21%
Chance of exploitation in the next 30 days. Higher than 11.48% of scored vulnerabilities.
Known exploited
No
Not in CISA's catalog
Weakness
CWE-471 Modification of Assumed-Immutable Data (MAID)
Fix
Available
3 packages have a fixed version

Description

Impact

The tf.raw_ops.ImmutableConst operation returns a constant tensor created from a memory mapped file which is assumed immutable. However, if the type of the tensor is not an integral type, the operation crashes the Python interpreter as it tries to write to the memory area:

>>> import tensorflow as tf
>>> with open('/tmp/test.txt','w') as f: f.write('a'*128)
>>> tf.raw_ops.ImmutableConst(dtype=tf.string,shape=2,
                              memory_region_name='/tmp/test.txt')

If the file is too small, TensorFlow properly returns an error as the memory area has fewer bytes than what is needed for the tensor it creates. However, as soon as there are enough bytes, the above snippet causes a segmentation fault.

This is because the alocator used to return the buffer data is not marked as returning an opaque handle since the needed virtual method is not overriden.

Patches

We have patched the issue in GitHub commit c1e1fc899ad5f8c725dcbb6470069890b5060bc7 and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.

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
< 1.15.5
1.15.5
tensorflow
>= 2.0.0, < 2.0.4
2.0.4
tensorflow
>= 2.1.0, < 2.1.3
2.1.3
tensorflow
>= 2.2.0, < 2.2.2
2.2.2
tensorflow
>= 2.3.0, < 2.3.2
2.3.2
tensorflow
< 1.15.5
>= 2.0.0, < 2.0.4
>= 2.1.0, < 2.1.3
>= 2.2.0, < 2.2.2
>= 2.3.0, < 2.3.2
1.15.5
2.0.4
2.1.3
2.2.2
2.3.2
tensorflow-cpu
< 1.15.5
1.15.5
tensorflow-cpu
>= 2.0.0, < 2.0.4
2.0.4
tensorflow-cpu
>= 2.1.0, < 2.1.3
2.1.3
tensorflow-cpu
>= 2.2.0, < 2.2.2
2.2.2
tensorflow-cpu
>= 2.3.0, < 2.3.2
2.3.2
tensorflow-cpu
< 1.15.5
>= 2.0.0, < 2.0.4
>= 2.1.0, < 2.1.3
>= 2.2.0, < 2.2.2
>= 2.3.0, < 2.3.2
1.15.5
2.0.4
2.1.3
2.2.2
2.3.2
tensorflow-gpu
< 1.15.5
1.15.5
tensorflow-gpu
>= 2.0.0, < 2.0.4
2.0.4
tensorflow-gpu
>= 2.1.0, < 2.1.3
2.1.3
tensorflow-gpu
>= 2.2.0, < 2.2.2
2.2.2
tensorflow-gpu
>= 2.3.0, < 2.3.2
2.3.2
tensorflow-gpu
< 1.15.5
>= 2.0.0, < 2.0.4
>= 2.1.0, < 2.1.3
>= 2.2.0, < 2.2.2
>= 2.3.0, < 2.3.2
1.15.5
2.0.4
2.1.3
2.2.2
2.3.2

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 → 1.15.5

pip
pip install --upgrade "tensorflow>=1.15.5"
poetry
poetry add "tensorflow>=1.15.5"
uv
uv add "tensorflow>=1.15.5"

tensorflow-cpu PyPI → 1.15.5

pip
pip install --upgrade "tensorflow-cpu>=1.15.5"
poetry
poetry add "tensorflow-cpu>=1.15.5"
uv
uv add "tensorflow-cpu>=1.15.5"

tensorflow-gpu PyPI → 1.15.5

pip
pip install --upgrade "tensorflow-gpu>=1.15.5"
poetry
poetry add "tensorflow-gpu>=1.15.5"
uv
uv add "tensorflow-gpu>=1.15.5"

Severity breakdown

CVSS 4.0 0.0

CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:N/VI:L/VA:L/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)
Low
Availability (vulnerable system)
Low
Confidentiality (subsequent systems)
None
Integrity (subsequent systems)
None
Availability (subsequent systems)
None

CVSS 3.1 4.4

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

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

Weakness

CWE-471: Modification of Assumed-Immutable Data (MAID)

The product does not properly protect an assumed-immutable element from being modified by an attacker.

How to prevent it
  • Architecture and Design, Operation, Implementation: When the data is stored or transmitted through untrusted sources that could modify the data, implement integrity checks to detect unauthorized modification, or store/transmit the data in a trusted location that is free from external influence.

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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