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.
Detected presence of files with behaviors similar to malicious packages published on PyPI.
Causes risk: suspicious application behaviors
hunting
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
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 removed from the public package repository.
Causes risk: components prone to hijacking
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. 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.
Top behaviors
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 URLs that link to interesting file formats.
network
Prevalence in PyPI community
Behavior often found in this community (Common)
76 packages
found in
Top 100
459 packages
found in
Top 1k
3531 packages
found in
Top 10k
105.97k packages
in community
Creates a process.
execution
Prevalence in PyPI community
Behavior often found in this community (Common)
69 packages
found in
Top 100
506 packages
found in
Top 1k
3612 packages
found in
Top 10k
163.85k packages
in community
The software package does not declare any source code repository.
anomaly
Prevalence in PyPI community
Behavior often found in this community (Common)
70 packages
found in
Top 100
472 packages
found in
Top 1k
4207 packages
found in
Top 10k
413.79k packages
in community
Writes data to the STDOUT stream.
execution
Prevalence in PyPI community
Behavior often found in this community (Common)
89 packages
found in
Top 100
714 packages
found in
Top 1k
6590 packages
found in
Top 10k
461.31k packages
in community
Top vulnerabilities
No vulnerabilities found.