CVE-2020-15197
Vulnerability Scoring
Status: Analyzed - Last modified: 17 Aug 2021, 13:21 UTC Published: 25 Sep 2020, 19:15 UTC
In 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.
Above is the Access Complexity Graph for CVE-2020-15197. It helps visualize the difficulty level and privilege requirements needed to exploit this vulnerability, providing a quick assessment of its exploitation feasibility.
Above is the CVSS Sub-score Breakdown for CVE-2020-15197, illustrating how Base, Impact, and Exploitability factors combine to form the overall severity rating. A higher sub-score typically indicates a more severe or easier-to-exploit vulnerability.
Below is the Impact Analysis for CVE-2020-15197, showing how Confidentiality, Integrity, and Availability might be affected if the vulnerability is exploited. Higher values usually signal greater potential damage.
The EPSS score estimates the probability that this vulnerability will be exploited in the near future.
EPSS Score: 0.135% (probability of exploit)
EPSS Percentile: 50.01%
(lower percentile = lower relative risk)
This vulnerability is less risky than approximately 49.99% of others.
nvd@nist.gov
Primary
CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H
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