List of software quality issues with the number of affected components.
category ALL
Policies
Info
Category
Problem
Threat researchers have manually inspected the software package and determined that it contains one or more malicious files. The detection was made by a hash-based file reputation lookup. This malware detection method is considered highly accurate, and can typically identify the malware family by name.
Prevalence in PyPI community
0 packages
found in
Top 100
0 packages
found in
Top 1k
10 packages
found in
Top 10k
14.02k packages
in community
Next steps
Investigate the build and release environment for software supply chain compromise.
Avoid using this software package.
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 exclusively used by malicious software with the intent to cause harm. When a software package matches behavior traits of malicious software, it becomes flagged by security solutions. It is highly likely that the software package was tampered with by a malicious actor or a rogue insider. Detected threat type matches the behaviors typically exhibited by the infostealer malware profile. Infostealers are commonly used to steal sensitive user data such as stored login details, financial information, and other personally identifiable information.
Prevalence in PyPI community
0 packages
found in
Top 100
0 packages
found in
Top 1k
3 packages
found in
Top 10k
2.05k packages
in community
Next steps
Investigate reported detections.
Investigate your build and release environment for software supply chain compromise.
You should delay the software release until the investigation is completed.
In the case this behavior is intended, rewrite the flagged code without using the malware-like behaviors.
Problem
Most software applications use standardized installation formats for their distribution. Software installers are built from instructions written within installation scripts that act as blueprints for the distribution format assembly. Installation scripts declare the most important software properties, such as the default installation location, its external dependencies, and various actions that may occur during the installation process. Actions defined within the installation script are executed automatically during events such as software deployment, update, or removal. These events are used by software developers to set up the environment for nominal software use, or to perform cleanup upon software removal. It is unusual for certain types of software installers to execute commands that can collect sensitive system information. Attackers often abuse software installers to run commands that collect identifiable system information such as hostnames, user names, folder structures, and other data points that could help them understand the environment in which their malicious code was installed.
Prevalence in PyPI community
0 packages
found in
Top 100
1 packages
found in
Top 1k
3 packages
found in
Top 10k
706 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 installation procedure without using the marked behaviors.
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. Python Package Index (PyPI) repository is often abused by threat actors to publish software packages that exhibit malicious behaviors. Malware authors use numerous tactics to lure developers into including malicious PyPI packages in their software projects. Most malicious packages published on PyPI target developers and their workstations. However, some are designed to activate only when deployed in the end-user environment. Both types of Python malicious packages are detected by proprietary ReversingLabs threat hunting algorithms. This detection method is considered proactive, and it is based on Machine Learning (ML) algorithms that can detect novel malware. The detection is strongly influenced by behaviors that software components exhibit. Behaviors similar to previously discovered malware and software supply chain attacks may cause some otherwise benign software packages to be detected by this policy.
Prevalence in PyPI community
1 packages
found in
Top 100
18 packages
found in
Top 1k
104 packages
found in
Top 10k
16.5k 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
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
Out-of-band application security testing tools (OAST) rely on external servers to detect security vulnerabilities in web applications. This form of security testing inspects the web application from the outside in, similar to how the attackers would probe its defenses. External domains are commonly used to facilitate out-of-band security testing. Attackers commonly abuse tools designed for security testing to monitor network traffic and find weaknesses that can be exploited. While the presence of domains related to OAST tools does not imply malicious intent, all of their uses in a software package should be documented and approved. Attackers might have purposely injected security testing tools in the software package to monitor the network traffic of the infected computer system. It is also possible that the software package has mistakenly included a part of its testing infrastructure during packaging.
Prevalence in PyPI community
0 packages
found in
Top 100
2 packages
found in
Top 1k
3 packages
found in
Top 10k
673 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. Open source communities depend on the work of thousands of software developers that volunteer their time to maintain software components. Software developers build up the reputation of their open source projects by developing in public. Modern source code repositories have many social features that allow software developers to handle bug reports, have discussions with their users, and convey reaching significant project milestones. It is uncommon to find open source projects that omit linking their component to a publicly accessible source code repository.
Prevalence in PyPI community
70 packages
found in
Top 100
472 packages
found in
Top 1k
4207 packages
found in
Top 10k
413.79k 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.