Denial of Service (DoS) Affecting tensorflow-cpu package, versions [2.5.0,2.5.1) [2.4.0,2.4.3) [,2.3.4)

tensorflow-cpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). The implementation of tf.raw_ops.SparseReshape can be made to trigger an integral division by 0 exception. The implementation calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The reshape functor blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0.

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 ws package

• GitHub Commit