List of software quality issues with the number of affected components.
category ALL
Policies
Info
Category
Problem
Proprietary ReversingLabs malware detection algorithms have determined that the software package contains one or more malicious components. The detection was made by either a static byte signature, software component identity, or a complete file hash. This malware detection method is considered highly accurate, and can typically attribute malware to previously discovered software supply chain attacks. It is common to have multiple supply chain attack artifacts that relate to a single malware incident.
Prevalence in PyPI community
0 packages
found in
Top 100
2 packages
found in
Top 1k
18 packages
found in
Top 10k
15.82k packages
in community
Next steps
If the software intent does not relate to malicious behavior, investigate the build and release environment for software supply chain compromise.
Avoid using this software package.
Problem
Proprietary ReversingLabs malware detection algorithms have determined that the software package contains one or more malicious files. The detection was made by a machine learning model. This malware detection method is considered proactive, and can typically identify the malware threat type. 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 components to be detected as malicious.
Prevalence in PyPI community
0 packages
found in
Top 100
2 packages
found in
Top 1k
12 packages
found in
Top 10k
708 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.
Avoid using this software package until it is vetted as safe.
Consider rewriting code that may have triggered the detection due to its malware similarity.
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
1 packages
found in
Top 1k
15 packages
found in
Top 10k
15.24k packages
in community
Next steps
Investigate the build and release environment for software supply chain compromise.
Avoid using this software package.
Problem
Third-party malware detection algorithms have determined that the software package contains one or more malicious files. The detection was made by a hash-based file reputation lookup. This malware detection method is considered accurate, and can typically identify the malware family by name.
Prevalence in PyPI community
1 packages
found in
Top 100
2 packages
found in
Top 1k
23 packages
found in
Top 10k
1.08k packages
in community
Next steps
If the software intent does not relate to malicious behavior, investigate the build and release environment for software supply chain compromise.
Avoid using this software package.
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 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
6 packages
found in
Top 10k
2.63k 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
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. The ReversingLabs community package index may place software packages into quarantine during an ongoing security incident investigation. When a software project is placed into quarantine, it is typically unavailable for installation. Additionally, repository maintainers may also hide quarantined software projects from users that might stumble upon them using repository search functions. Quarantine is typically raised for software projects that were reported for unusual code changes or unexpected software behaviors.
Prevalence in PyPI community
1 packages
found in
Top 100
7 packages
found in
Top 1k
26 packages
found in
Top 10k
21.51k 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 they are well-documented.
Avoid using this software package until it is vetted as safe.
Problem
Third-party malware detection algorithms have determined that the software package contains one or more suspicious files. The detection was made by a hash-based file reputation lookup. This malware detection method is considered predictive, and can typically identify the malware family by name.
Prevalence in PyPI community
4 packages
found in
Top 100
5 packages
found in
Top 1k
37 packages
found in
Top 10k
475 packages
in community
Next steps
Suspicious detections are a lower confidence detection, so you should first review them for malicious intent.
If the software intent does not relate to malicious behavior, investigate the build and release environment for software supply chain compromise.
Proceed with increased caution when using this software package.
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 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
0 packages
found in
Top 100
7 packages
found in
Top 1k
48 packages
found in
Top 10k
22.29k 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.
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