Denial of Service (DoS) Affecting tensorflow package, versions [,2.5.3) [2.6.0,2.6.3) [2.7.0,2.7.1)
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Test your applications- Snyk ID SNYK-PYTHON-TENSORFLOW-2395404
- published 6 Feb 2022
- disclosed 6 Feb 2022
- credit Unknown
Introduced: 6 Feb 2022
CVE-2022-23588 Open this link in a new tabHow to fix?
Upgrade tensorflow to version 2.5.3, 2.6.3, 2.7.1 or higher.
Overview
tensorflow is a machine learning framework.
Affected versions of this package are vulnerable to Denial of Service (DoS) due to a CHECK-fail in the Tensor constructor as reference types are not allowed. A malicious user can cause this by altering a SavedModel such that Grappler optimizer would attempt to build a tensor using a reference dtype.
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.
Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.
One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.
When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.
Two common types of DoS vulnerabilities:
High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.
Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm
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