List of software quality issues with the number of affected components.
category ALL
Policies
Info
Category
Problem
Operating systems execute application code in multiple privilege access levels. Separation of privileges is designed to protect the stability and integrity of the operating system by shielding it from issues that the user run applications may cause. However, some users may need to interact with higher privilege parts of the operating system to accomplish specific tasks. For this purpose, operating systems provide facilities that users may leverage to temporarily elevate their running privileges. Users with higher privileges can run any application with the same privilege level as their own. Attackers often try to trick privileged users into running malicious code, enabling them to infect the operating system. While the presence of code that elevates user privileges does not necessarily imply malicious intent, all of its uses in a software package should be documented and approved. Only select applications should consider using functions that can elevate user privileges. One example of acceptable use for such functions is allowing the users to install software packages and updates.
Prevalence in PyPI community
1 packages
found in
Top 100
31 packages
found in
Top 1k
194 packages
found in
Top 10k
4.1k packages
in community
Next steps
Investigate reported detections as indicators of software tampering.
Consult Mitre ATT&CK documentation: T1548 - Abuse Elevation Control Mechanism.
Consider rewriting the flagged code without using the marked behaviors.
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
0 packages
found in
Top 100
1 packages
found in
Top 1k
20 packages
found in
Top 10k
83.87k 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
0 packages
found in
Top 100
0 packages
found in
Top 1k
5 packages
found in
Top 10k
38.63k 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 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
1 packages
found in
Top 100
13 packages
found in
Top 1k
37 packages
found in
Top 10k
443.06k 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
14 packages
found in
Top 100
85 packages
found in
Top 1k
513 packages
found in
Top 10k
8.12k 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
24 packages
found in
Top 100
130 packages
found in
Top 1k
733 packages
found in
Top 10k
14.31k 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
10 packages
found in
Top 100
67 packages
found in
Top 1k
398 packages
found in
Top 10k
9.13k 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 components contain executable code that performs actions implemented during its development. These actions are called behaviors. In the analysis report, behaviors are presented as human-readable descriptions that best match the underlying code intent. While most behaviors are benign, some are commonly abused by malicious software with the intent to cause harm. When a software package shares behavior traits with malicious software, it may become flagged by security solutions. Any detection from security solutions can cause friction for the end-users during software deployment. While the behavior is likely intended by the developer, there is a small chance this detection is true positive, and an early indication of a software supply chain attack.
Prevalence in PyPI community
20 packages
found in
Top 100
92 packages
found in
Top 1k
907 packages
found in
Top 10k
43.88k packages
in community
Next steps
Investigate reported detections.
If the software intent does not relate to the reported behavior, investigate your build and release environment for software supply chain compromise.
You should delay the software release until the investigation is completed, or until the issue is risk accepted.
Consider rewriting the flagged code without using the marked behaviors.
Problem
Uniform Resource Locators (URLs) are structured addresses that point to locations and assets on the internet. URLs allow software developers to build complex applications that exchange data with servers that can be hosted in multiple geographical regions. URLs can commonly be found embedded in documentation, configuration files, source code and compiled binaries. Digital currencies (cryptocurrencies) use a peer-to-peer technology to facilitate instant payments on the web. Coinmining is the process of creating new digital currency coins by solving extremely complicated math problems that verify transactions in the currency exchange. When a cryptocurrency coin is successfully mined, the miner receives a predetermined amount of digital currency. Coinmining is a lucrative but expensive venture. For that reason, malicious actors often try to hijack computer systems with the aim to make others cover the mining cost. While the presence of coinmining references does not imply malicious intent, all of its uses in a software package should be documented and approved. Only select applications should consider mining for digital currency. An increasing number of software supply chain attacks in the open source space try to install unauthorized coinmining software.
Prevalence in PyPI community
0 packages
found in
Top 100
0 packages
found in
Top 1k
3 packages
found in
Top 10k
123 packages
in community
Next steps
Investigate reported detections.
If the software should not include these network references, investigate your build and release environment for software supply chain compromise.
You should delay the software release until the investigation is completed, or until the issue is risk accepted.
Consider removing all references to flagged network locations.
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
1 packages
found in
Top 100
11 packages
found in
Top 1k
910 packages
found in
Top 10k
717.09k 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.