CVE-2020-15197

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Summary

CVECVE-2020-15197
StatePUBLIC
Assigner[email protected]
Source PriorityCVE Program / NVD first with legacy fallback
Published2020-09-25 19:15:00 UTC
Updated2021-08-17 13:21:00 UTC
DescriptionIn Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix. However, malicious users can pass in tensors of different rank, resulting in a `CHECK` assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

Risk And Classification

Problem Types: CWE-20 | CWE-617

NVD Known Affected Configurations (CPE 2.3)

TypeVendorProductVersionUpdateEditionLanguage
Application Google Tensorflow 2.3.0 All All All
Application Tensorflow Tensorflow 2.3.0 All All All
Application Tensorflow Tensorflow 2.3.0 All All All

References

ReferenceSourceLinkTags
Fix multiple vulnerabilities in `tf.raw_ops.*CountSparseOutput`. · tensorflow/tensorflow@3cbb917 · GitHub MISC github.com Patch, Third Party Advisory
Crash due to invalid splits in SparseCountSparseOutput · Advisory · tensorflow/tensorflow · GitHub CONFIRM github.com Exploit, Third Party Advisory
Release TensorFlow 2.3.1 · tensorflow/tensorflow · GitHub MISC github.com Third Party Advisory
CVE Program record CVE.ORG www.cve.org canonical
NVD vulnerability detail NVD nvd.nist.gov canonical, analysis

Legacy QID Mappings

  • 981470 Python (pip) Security Update for tensorflow-gpu (GHSA-qc53-44cj-vfvx)

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