Top issues
Detected presence of known software supply chain attack artifacts.
Causes risk: supply chain attack artifacts
threats
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
0 packages
found in
Top 1k
11 packages
found in
Top 10k
14.01k 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.
Detected presence of malicious files through analyst-vetted file reputation.
Causes risk: analyst-vetted malware found
threats
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
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. One or more embedded URLs were discovered to link to raw files hosted on GitHub. Attackers often abuse popular web services to host malicious payloads. Since code-sharing services URLs are typically allowed by security solutions, using them for payload delivery increases the odds that the malicious code will reach the user. While the presence of code-sharing service locations does not imply malicious intent, all of their uses in a software package should be documented and approved. An increasing number of software supply chain attacks in the open source space leverages the GitHub service to deliver malicious payloads.Prevalence in PyPI community
36 packages
found in
Top 100
219 packages
found in
Top 1k
1678 packages
found in
Top 10k
63.41k 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 an alternative delivery mechanism for software packages.
Detected presence of software components that were recently published to the public package repository.
hunting
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.
Detected presence of software components that have low popularity or number of downloads.
hunting
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.
Top behaviors
Decodes data using the Base64 algorithm.
packer
Prevalence in PyPI community
Behavior often found in this community (Common)
39 packages
found in
Top 100
252 packages
found in
Top 1k
1477 packages
found in
Top 10k
53.15k packages
in community
Contains URLs that link to raw files on GitHub.
network
Prevalence in PyPI community
Behavior often found in this community (Common)
36 packages
found in
Top 100
219 packages
found in
Top 1k
1678 packages
found in
Top 10k
63.42k packages
in community
Contains unusually long strings.
anomaly
Prevalence in PyPI community
Behavior often found in this community (Common)
51 packages
found in
Top 100
408 packages
found in
Top 1k
2829 packages
found in
Top 10k
106.17k packages
in community
Opens a URL.
network
Prevalence in PyPI community
Behavior often found in this community (Common)
30 packages
found in
Top 100
166 packages
found in
Top 1k
1155 packages
found in
Top 10k
31.66k packages
in community
Calculates the SHA-256 hash of data.
file
Prevalence in PyPI community
Behavior often found in this community (Common)
24 packages
found in
Top 100
199 packages
found in
Top 1k
959 packages
found in
Top 10k
27.04k packages
in community
Top vulnerabilities
No vulnerabilities found.