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. Open source projects are the intellectual property of their respective authors. At any time, the authors may choose to completely remove the software component from a public repository. This often occurs when a software project reaches its end-of-life stage, or when the software authors lose interest in maintaining the project. This kind of removal frees up the software package name, its unique software identifier in the public repository, for other developers to use. However, new software project owners might have malicious intent. Threat actors are continuously monitoring popular package names in case their unique identifiers suddenly become available for hijacking. Once the software projects falls under new ownership, the new maintainers may opt to use the project popularity to spread malware to unsuspecting users.
Prevalence in PyPI community
2 packages
found in
Top 100
12 packages
found in
Top 1k
54 packages
found in
Top 10k
99.05k packages
in community
Next steps
Inspect behaviors exhibited by the detected software components.
If the software behaviors differ from expected, investigate the build and release environment for software supply chain compromise.
Revise the use of components that raise these alarms. If you can't deprecate those components, make sure that their versions are pinned.
Avoid using this software package until it is vetted as safe.
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
Buffer overrun protection on Linux is achieved in two ways. The most common solution is to use the stack canary (also called cookie). The stack canary is a special value written onto the stack that allows the operating system to detect and terminate the program if a stack overrun occurs. In most cases, compilers will apply the stack canary conservatively in order to avoid a negative performance impact. Therefore, stack canaries are often used together with another stack overrun mitigation - fortified functions. Fortified functions are usually wrappers around standard glibc functions (such as memcpy) which perform boundary checks either at compile time or run time to determine if a memory violation has occurred. The compiler needs additional context to generate such calls (for example, array size that needs to be known at compile time). Because of this, the compiler will virtually never substitute all viable functions with their fortified counterparts in complex programs. However, when combined with the stack canary, fortified functions provide a good measure of buffer overrun protection.
Prevalence in PyPI community
16 packages
found in
Top 100
97 packages
found in
Top 1k
558 packages
found in
Top 10k
12.31k packages
in community
Next steps
Presence of unfortified string functions may indicate use of unsafe programming practices, and you should avoid it if possible.
In GCC, enable fortified functions with -fstack-protector and -D_FORTIFY_SOURCE=2 flag, while using at least -O1 optimization level.
Problem
Buffer overrun protection on Linux is achieved in two ways. The most common solution is to use the stack canary (also called cookie). The stack canary is a special value written onto the stack that allows the operating system to detect and terminate the program if a stack overrun occurs. In most cases, compilers will apply the stack canary conservatively in order to avoid a negative performance impact. Therefore, stack canaries are often used together with another stack overrun mitigation - fortified functions. Fortified functions are usually wrappers around standard glibc functions (such as memcpy) which perform boundary checks either at compile time or run time to determine if a memory violation has occurred. The compiler needs additional context to generate such calls (for example, array size that needs to be known at compile time). Because of this, the compiler will virtually never substitute all viable functions with their fortified counterparts in complex programs. However, when combined with the stack canary, fortified functions provide a good measure of buffer overrun protection.
Prevalence in PyPI community
25 packages
found in
Top 100
142 packages
found in
Top 1k
797 packages
found in
Top 10k
24.6k packages
in community
Next steps
Presence of unfortified memory functions may indicate use of unsafe programming practices, and you should avoid it if possible.
In GCC, enable fortified functions with -fstack-protector and -D_FORTIFY_SOURCE=2 flag, while using at least -O1 optimization level.
Problem
Buffer overrun protection on Linux is achieved in two ways. The most common solution is to use the stack canary (also called cookie). The stack canary is a special value written onto the stack that allows the operating system to detect and terminate the program if a stack overrun occurs. In most cases, compilers will apply the stack canary conservatively in order to avoid a negative performance impact. Therefore, stack canaries are often used together with another stack overrun mitigation - fortified functions. Fortified functions are usually wrappers around standard glibc functions (such as memcpy) which perform boundary checks either at compile time or run time to determine if a memory violation has occurred. The compiler needs additional context to generate such calls (for example, array size that needs to be known at compile time). Because of this, the compiler will virtually never substitute all viable functions with their fortified counterparts in complex programs. However, when combined with the stack canary, fortified functions provide a good measure of buffer overrun protection.
Prevalence in PyPI community
11 packages
found in
Top 100
76 packages
found in
Top 1k
500 packages
found in
Top 10k
17.85k packages
in community
Next steps
Presence of some input functions may indicate use of unsafe programming practices, and you should avoid it if possible.
In GCC, enable fortified functions with -fstack-protector and -D_FORTIFY_SOURCE=2 flag, while using at least -O1 optimization level.
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
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 using components that are rarely used to build applications 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
7 packages
found in
Top 100
34 packages
found in
Top 1k
839 packages
found in
Top 10k
928.02k 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.