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failIncident: Removal
Scanned: 4 days ago

stubsout

Artifact:
latest
removed
Telemetry research package for dependency analysis.
License: Permissive (MIT)
Published: 6 months ago



SAFE Assessment

Compliance

Licenses
No license compliance issues
Secrets
No sensitive information found

Security

Vulnerabilities
No known vulnerabilities detected
Hardening
No application hardening issues

Threats

Tampering
1 malware-like behaviors found
Malware
No evidence of malware inclusion

INCIDENTS FOR THIS VERSION:

removal
3 months agoReported By: Community

Popularity

145
Total Downloads
Contributor
Declared Dependencies
0
Dependents

Top issues

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.

Prevalence in PyPI community

0 packages
found in
Top 100
0 packages
found in
Top 1k
0 packages
found in
Top 10k
26 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. Open source communities depend on the work of thousands of software developers that volunteer their time to maintain software components. While the majority of open source contributors are altruistic and trustworthy, some software developers are also members of security research or bug bounty programs. Researchers that participate in bug bounty programs develop applications that leak sensitive environment information to prove that they've successfully bypassed security mechanisms. Code written by these software developers should be put under a higher degree of scrutiny, and their code should never appear in software packages intended for release.

Prevalence in PyPI community

0 packages
found in
Top 100
0 packages
found in
Top 1k
0 packages
found in
Top 10k
78 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.
Consider removing the software 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

47 packages
found in
Top 100
226 packages
found in
Top 1k
1735 packages
found in
Top 10k
74797 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 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

No prevalence information at this time

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

No prevalence information at this time

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

Prevalence in PyPI community

Behavior exclusively used by malicious software (Malicious)
Behavior uncommon for this community (Uncommon)
0 packages
found in
Top 100
0 packages
found in
Top 1k
0 packages
found in
Top 10k
78 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
39 packages
found in
Top 100
280 packages
found in
Top 1k
1754 packages
found in
Top 10k
67026 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
68 packages
found in
Top 100
508 packages
found in
Top 1k
3570 packages
found in
Top 10k
165477 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
68 packages
found in
Top 100
540 packages
found in
Top 1k
3644 packages
found in
Top 10k
154572 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
43 packages
found in
Top 100
264 packages
found in
Top 1k
1666 packages
found in
Top 10k
47165 packages
in community

Top vulnerabilities

No vulnerabilities found.