What is the CVSS score of CVE-2020-15197?

CVE-2020-15197 has a CVSS 3.1 base score of 6.3 (Medium). CVSS vector: CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H. EPSS exploitation probability: 0.7%.

Is there a public PoC exploit available for CVE-2020-15197?

Yes, a public proof-of-concept exploit is available for CVE-2020-15197. Prioritise patching immediately. EPSS: 0.7%.

Is there a patch available for CVE-2020-15197?

Yes, a patch is available for CVE-2020-15197. Update the affected software to the latest version immediately.

Tensorflow CVE-2020-15197

Q: Which versions of Tensorflow are affected by CVE-2020-15197?

In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor.. A patch is available.

MEDIUM

Improper Input Validation (CWE-20)

2020-09-25 security-advisories@github.com

Denial Of Service Tensorflow

6.3

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

6.3 MEDIUM

AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H

Attack Vector

Network

Attack Complexity

High

Privileges Required

Low

User Interaction

None

Scope

Changed

Confidentiality

None

Integrity

None

Availability

High

Lifecycle Timeline

CVE Published

Sep 25, 2020 - 19:15 nvd

MEDIUM 6.3

Blast Radius

ecosystem impact

† from your stack dependencies † transitive graph · vuln.today resolves 4-path depth

64 pypi packages depend on tensorflow (62 direct, 2 indirect)
1 pypi packages depend on tensorflow-gpu (1 direct, 0 indirect)

Ecosystem-wide dependent count for version 2.3.0 and other introduced versions.

DescriptionNVD

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.

AnalysisAI

In Tensorflow before version 2.3.1, the SparseCountSparseOutput implementation does not validate that the input arguments form a valid sparse tensor. Rated medium severity (CVSS 6.3), this vulnerability is remotely exploitable. Public exploit code available.

Technical ContextAI

This vulnerability is classified under CWE-20. 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. Affected products include: Google Tensorflow. Version information: version 2.3.1.

RemediationAI

A vendor patch is available. Apply the latest security update as soon as possible. Apply vendor patches when available. Implement network segmentation and monitoring as interim mitigations.