What is the CVSS score of CVE-2020-15206?

CVE-2020-15206 has a CVSS 3.1 base score of 7.5 (High). CVSS vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H. EPSS exploitation probability: 0.9%.

Is there a public PoC exploit available for CVE-2020-15206?

Yes, a public proof-of-concept exploit is available for CVE-2020-15206. Prioritise patching immediately. EPSS: 0.9%.

Is there a patch available for CVE-2020-15206?

Yes, a patch is available for CVE-2020-15206. Update the affected software to the latest version immediately.

Tensorflow CVE-2020-15206

HIGH

Improper Input Validation (CWE-20)

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

Denial Of Service Tensorflow Leap

7.5

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

7.5 HIGH

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

Primary rating from NVD · only source for this CVE.

CVSS VectorNVD

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

Attack Vector

Network

Attack Complexity

Low

Privileges Required

None

User Interaction

None

Scope

Unchanged

Confidentiality

None

Integrity

None

Availability

High

Lifecycle Timeline

CVE Published

Sep 25, 2020 - 19:15 nvd

HIGH 7.5

Blast Radius

ecosystem impact

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

46 pypi packages depend on tensorflow (41 direct, 5 indirect)
11 pypi packages depend on tensorflow-gpu (11 direct, 0 indirect)

Ecosystem-wide dependent count for version 2.0.0 and other introduced versions.

DescriptionNVD

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's SavedModel protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using tensorflow-serving or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

AnalysisAI

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's SavedModel protocol buffer and altering the name of required keys results in segfaults and data. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.

Technical ContextAI

This vulnerability is classified under CWE-20. In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's SavedModel protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using tensorflow-serving or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. Affected products include: Google Tensorflow, Opensuse Leap.

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.