GHSA-c9f3-9wfr-wgh7 BIT-tensorflow-2020-26267 CVE-2020-26267 PYSEC-2020-140 PYSEC-2020-298 PYSEC-2020-333

Lack of validation in data format attributes in TensorFlow

Medium PyPI

Severity: 0.0 / 10; Medium · CVSS 4.0
Exploit chance (EPSS): 0.24%; Chance of exploitation in the next 30 days. Higher than 15.60% of scored vulnerabilities.
Known exploited: No; Not in CISA's catalog
Weakness: CWE-125 Out-of-bounds Read
Fix: Available; 3 packages have a fixed version

Description

Impact

The tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC.

However, these assumptions are not checked and this can result in uninitialized memory accesses, read outside of bounds and even crashes.

>>> import tensorflow as tf
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='1234', dst_format='1234')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 757100143], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='HHHH', dst_format='WWWW')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='H', dst_format='W')
<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)>
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], 
                                    src_format='1234', dst_format='1253')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 2, 939037184, 3], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='1234', dst_format='1223')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 32701, 2, 3], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='1224', dst_format='1423')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([1, 4, 3, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='432')
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 3, 2, 32701], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],
                                    src_format='12345678', dst_format='87654321')
munmap_chunk(): invalid pointer
Aborted
...
>>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]],           
                                    src_format='12345678', dst_format='87654321')
<tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[71364624,        0],
       [71365824,        0],
       [     560,        0],
       [      48,        0]], dtype=int32)>
...
>>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]], 
                                    src_format='12345678', dst_format='87654321')
free(): invalid next size (fast)
Aborted

A similar issue occurs in tf.raw_ops.DataFormatDimMap, for the same reasons:

>>> tf.raw_ops.DataFormatDimMap(x=[[1,5],[2,6],[3,7],[4,8]], src_format='1234',
>>> dst_format='8765')
<tf.Tensor: shape=(4, 2), dtype=int32, numpy=
array([[1954047348, 1954047348],
       [1852793646, 1852793646],
       [1954047348, 1954047348],
       [1852793632, 1852793632]], dtype=int32)>

Patches

We have patched the issue in GitHub commit ebc70b7a592420d3d2f359e4b1694c236b82c7ae 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

Package	Vulnerable	Fixed 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:P/PR:N/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N

Attack vector: Local
Attack complexity: Low
Attack requirements: Present
Privileges required: None
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-125: Out-of-bounds Read

The product reads data past the end, or before the beginning, of the intended buffer.

How to prevent it

Implementation: Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does. When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue." Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright. To reduce the likelihood of introducing an out-of-bounds read, ensure that you validate and ensure correct calculations for any length argument, buffer size calculation, or offset. Be especially careful of relying on a sentinel (i.e. special character such as NUL) in untrusted inputs.
Architecture and Design: Use a language that provides appropriate memory abstractions.

References

Advisories

nvd.nist.gov/vuln/detail/CVE-2020-26267

Packages

github.com/tensorflow/tensorflow

Timeline

Dec 10, 2020 Published
Sep 10, 2026 Last updated by the source
Oct 31, 2026 Latest EPSS score

Advisory	Severity	Why 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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Description

Impact

Patches

For more information

Attribution

Affected versions

PyPI

How to fix

tensorflow PyPI → 1.15.5

tensorflow-cpu PyPI → 1.15.5

tensorflow-gpu PyPI → 1.15.5

Severity breakdown

CVSS 4.0 0.0

CVSS 3.1 4.4

Weakness

CWE-125: Out-of-bounds Read

References

Advisories

Packages

Other links

Timeline

Related advisories

tensorflow PyPI → `1.15.5`

tensorflow-cpu PyPI → `1.15.5`

tensorflow-gpu PyPI → `1.15.5`