Ecosyste.ms: Advisories
An open API service providing security vulnerability metadata for many open source software ecosystems.
Security Advisories: MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLXEyNjMtZnZ4bS1tNW13
Heap out of bounds access in MakeEdge in TensorFlow
Impact
Under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge
function creates an edge between one output tensor of the src
node (given by output_index
) and the input slot of the dst
node (given by input_index
). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType
values and comparing these for equality:
DataType src_out = src->output_type(output_index);
DataType dst_in = dst->input_type(input_index);
//...
However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays.
In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library.
Patches
We have patched the issue in GitHub commit 0cc38aaa4064fd9e79101994ce9872c6d91f816b 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.
Permalink: https://github.com/advisories/GHSA-q263-fvxm-m5mwJSON: https://advisories.ecosyste.ms/api/v1/advisories/MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLXEyNjMtZnZ4bS1tNW13
Source: GitHub Advisory Database
Origin: Unspecified
Severity: Moderate
Classification: General
Published: almost 4 years ago
Updated: 13 days ago
CVSS Score: 4.4
CVSS vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L
Identifiers: GHSA-q263-fvxm-m5mw, CVE-2020-26271
References:
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q263-fvxm-m5mw
- https://github.com/tensorflow/tensorflow/commit/0cc38aaa4064fd9e79101994ce9872c6d91f816b
- https://nvd.nist.gov/vuln/detail/CVE-2020-26271
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-302.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-337.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-257.yaml
- https://github.com/advisories/GHSA-q263-fvxm-m5mw
Blast Radius: 21.4
Affected Packages
pypi:tensorflow-gpu
Dependent packages: 155Dependent repositories: 11,499
Downloads: 430,308 last month
Affected Version Ranges: >= 2.3.0, < 2.3.2, >= 2.2.0, < 2.2.2, >= 2.1.0, < 2.1.3, >= 2.0.0, < 2.0.4, < 1.15.5
Fixed in: 2.3.2, 2.2.2, 2.1.3, 2.0.4, 1.15.5
All affected versions: 0.12.0, 0.12.1, 1.0.0, 1.0.1, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.9.0, 1.10.0, 1.10.1, 1.11.0, 1.12.0, 1.12.2, 1.12.3, 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.1.0, 2.1.1, 2.1.2, 2.2.0, 2.2.1, 2.3.0, 2.3.1
All unaffected versions: 1.15.5, 2.0.4, 2.1.3, 2.1.4, 2.2.2, 2.2.3, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.12.0
pypi:tensorflow-cpu
Dependent packages: 88Dependent repositories: 2,483
Downloads: 948,316 last month
Affected Version Ranges: >= 2.3.0, < 2.3.2, >= 2.2.0, < 2.2.2, >= 2.1.0, < 2.1.3, >= 2.0.0, < 2.0.4, < 1.15.5
Fixed in: 2.3.2, 2.2.2, 2.1.3, 2.0.4, 1.15.5
All affected versions: 1.15.0, 2.1.0, 2.1.1, 2.1.2, 2.2.0, 2.2.1, 2.3.0, 2.3.1
All unaffected versions: 2.1.3, 2.1.4, 2.2.2, 2.2.3, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1, 2.16.2, 2.17.0, 2.17.1, 2.18.0
pypi:tensorflow
Dependent packages: 2,172Dependent repositories: 73,755
Downloads: 18,843,694 last month
Affected Version Ranges: >= 2.3.0, < 2.3.2, >= 2.2.0, < 2.2.2, >= 2.1.0, < 2.1.3, >= 2.0.0, < 2.0.4, < 1.15.5
Fixed in: 2.3.2, 2.2.2, 2.1.3, 2.0.4, 1.15.5
All affected versions: 0.12.0, 0.12.1, 1.0.0, 1.0.1, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.9.0, 1.10.0, 1.10.1, 1.11.0, 1.12.0, 1.12.2, 1.12.3, 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.1.0, 2.1.1, 2.1.2, 2.2.0, 2.2.1, 2.3.0, 2.3.1
All unaffected versions: 1.15.5, 2.0.4, 2.1.3, 2.1.4, 2.2.2, 2.2.3, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1, 2.16.2, 2.17.0, 2.17.1, 2.18.0