Denial of Service (DoS) Affecting tensorflow-gpu package, versions [,1.15.5) [2.0.0, 2.0.4) [2.1.0, 2.1.3) [2.2.0, 2.2.2) [2.3.0, 2.3.2)


0.0
low

Snyk CVSS

    Attack Complexity High

    Threat Intelligence

    EPSS 0.04% (11th percentile)
Expand this section
NVD
3.3 low

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  • Snyk ID SNYK-PYTHON-TENSORFLOWGPU-1050407
  • published 11 Dec 2020
  • disclosed 10 Dec 2020
  • credit Unknown

How to fix?

Upgrade tensorflow-gpu to version 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 or higher.

Overview

tensorflow-gpu is a machine learning framework.

Affected versions of this package are vulnerable to Denial of Service (DoS). Running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer.

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 ws package

References