List of software quality issues with the number of affected components.
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Info
Category
Problem
Software composition analysis has identified a component with one or more known severe vulnerabilities. Available threat intelligence telemetry has confirmed that the reported high or critical severity vulnerabilities are actively being exploited by malicious actors.
Prevalence in PyPI community
40 packages
found in
Top 100
232 packages
found in
Top 1k
2014 packages
found in
Top 10k
115.63k packages
in community
Next steps
We strongly advise updating the component to the latest version.
If the update can't resolve the issue, create a plan to isolate or replace the affected component.
Problem
Software composition analysis has identified a component with one or more known vulnerabilities. Based on the CVSS scoring, these vulnerabilities have been marked as critical severity.
Prevalence in PyPI community
29 packages
found in
Top 100
174 packages
found in
Top 1k
1401 packages
found in
Top 10k
69.69k packages
in community
Next steps
Perform impact analysis for the reported CVEs.
We strongly advise updating the component to the latest version.
If the update can't resolve the issue, create a plan to isolate or replace the affected component.
Problem
Software composition analysis has identified a component with one or more known vulnerabilities. Based on the CVSS scoring, these vulnerabilities have been marked as high severity.
Prevalence in PyPI community
45 packages
found in
Top 100
313 packages
found in
Top 1k
2319 packages
found in
Top 10k
126.15k packages
in community
Next steps
Perform impact analysis for the reported CVEs.
Update the component to the latest version.
If the update can't resolve the issue, create a plan to isolate or replace the affected component.
Problem
Software developers use programming and design knowledge to build reusable software components. Software components are the basic building blocks for modern applications. Software consumed by an enterprise consists of hundreds, and sometimes even thousands of open source components. Software developers publish components they have authored to public repositories. While a new software project is a welcome addition to the open source community, it is not always prudent to indiscriminately use the latest components when building a commercial application. Irrespective of the software quality, the danger of being the first to try out a new project lies in the fact that the software component may contain novel, currently undetected malicious code. Therefore, it is prudent to review software component behaviors and even try out software component in a sandbox, an environment meant for testing untrusted code.
Prevalence in PyPI community
2 packages
found in
Top 100
22 packages
found in
Top 1k
623 packages
found in
Top 10k
614.08k packages
in community
Next steps
Check the software component behaviors for anomalies.
Consider exploratory software component testing within a sandbox environment.
Consider replacing the software component with a more widely used alternative.
Avoid using this software package until it is vetted as safe.
Problem
Software composition analysis has identified a component with one or more known vulnerabilities. Based on the CVSS scoring, these vulnerabilities have been marked as medium severity.
Prevalence in PyPI community
43 packages
found in
Top 100
210 packages
found in
Top 1k
2021 packages
found in
Top 10k
127.4k packages
in community
Next steps
Perform impact analysis for the reported CVEs.
Update the component to the latest version.
If the update can't resolve the issue, create a plan to isolate or replace the affected component.
Problem
Control Flow Guard (CFG/CFI) protects the code flow integrity by ensuring that dynamic calls are made only to vetted functions. Trusted execution paths rely on the ability of the operating system to build a list of valid function targets. Certain functions can intentionally be disallowed to prevent malicious code from deactivating vulnerability mitigation features. A list of such invalid function targets can include publicly exported symbols. Applications that enhance control flow integrity through export suppression rely on libraries to mark their publicly visible symbols as suppressed. This is done for all symbols that are considered to be sensitive functions, and to which access should be restricted. It is considered dangerous to mix applications that perform export suppression with libraries that do not.
Prevalence in PyPI community
30 packages
found in
Top 100
155 packages
found in
Top 1k
849 packages
found in
Top 10k
23.76k packages
in community
Next steps
To enable this mitigation on library code, refer to your programming language toolchain documentation.
In Microsoft VisualStudio, you can enable CFG mitigation by passing the /guard:cf parameter to the compiler and linker.
Problem
Control Flow Guard (CFG/CFI) protects the code flow integrity by ensuring that indirect calls are made only to vetted functions. This mitigation protects dynamically resolved function targets by instrumenting the code responsible for transferring execution control. Higher-level programming languages implement structured exception handling by managing their own code flow execution paths. As such, they are subject to code flow hijacking during runtime. Language-specific exception handling mitigation enforces execution integrity by instrumenting calls to manage execution context switching. Any deviation from the known and trusted code flow paths will cause the application to terminate. This makes malicious code less likely to execute.
Prevalence in PyPI community
36 packages
found in
Top 100
177 packages
found in
Top 1k
956 packages
found in
Top 10k
27.76k packages
in community
Next steps
It's highly recommended to enable this option for all software components used at security boundaries, or those that process user controlled inputs.
To enable this mitigation, refer to your programming language toolchain documentation.
In Microsoft VisualStudio, you can enable CFG mitigation by passing the /guard:cf parameter to the compiler and linker.
Problem
Software composition analysis has identified a component with one or more known vulnerabilities. Based on the CVSS scoring, these vulnerabilities have been marked as low severity.
Prevalence in PyPI community
22 packages
found in
Top 100
136 packages
found in
Top 1k
1255 packages
found in
Top 10k
76.42k packages
in community
Next steps
Perform impact analysis for the reported CVEs.
Update the component to the latest version.
Lower severity vulnerabilities can be resolved with less urgency, but you should still make a plan to do so.