Spectra Assure

failIncident: Malware

Scanned:

codefrequencychecker

Artifact:

latest

malicious

License: unknown

Published:

See on PyPI

SAFE Assessment

Compliance

Licenses

No license compliance issues

Secrets

1 active web service credentials

Security

Vulnerabilities

No known vulnerabilities detected

Hardening

No application hardening issues

Threats

Tampering

1 suspicious application behaviors

Malware

3 supply chain attack artifacts

INCIDENTS FOR THIS VERSION:

malware

Reported By: Community (OpenSSF)

More info

malware

Reported By: ReversingLabs (Researcher)

Learn more about malware detection

malware

N/AReported By: Community (kam193)

More info

List of software quality issues with the number of affected components.

Policies

Info

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.

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 as a Service (SaaS) platforms expose programmable interfaces to their authenticated users. These web services enable action automation and secure exchange of information. For authorization, users provide a unique token that confirms their access rights to the web service. Access tokens for supported web services are automatically validated via the least privilege APIs the service exposes. Detected tokens have been accepted as valid by the services they are associated with. This indicates they are currently active and may be abused if exposed to the public. Web service access tokens are considered secrets. They should never be included in a software release package, even if they are obfuscated by encryption on the client-side.

Prevalence in PyPI community

0 packages

found in

Top 100

0 packages

found in

Top 1k

2 packages

found in

Top 10k

248 packages

in community

Next steps

You should securely store web service access tokens, and fully automate their management and periodic rotation.

If tokens were published unintentionally and the software has been made public, you should revoke exposed tokens and file a security incident.

Examples of service tokens that may have been detected include AWS, Facebook, JWT, SWT, Slack and others.

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 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.

Problem

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.

Problem

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

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 using components that are rarely used to build applications 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

11 packages

found in

Top 1k

910 packages

found in

Top 10k

717.09k 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.

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.

Did you know...

... that you can use ReversingLabs’ Spectra Assure platform to perform regular risk assessments of packages you develop in-house?

We recommend continuous software safeness monitoring through CI/CD integrations and pre-release/deployment checks. In addition to basic package information found on this page our platform offers rich reports for developers and DevSecOps that help them remediate all reported issues.

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List of software quality issues with the number of affected components.

Policies

Info

Problem

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.

Problem

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

Prevalence in PyPI community

0 packages

found in

Top 100

0 packages

found in

Top 1k

2 packages

found in

Top 10k

248 packages

in community

Next steps

You should securely store web service access tokens, and fully automate their management and periodic rotation.

If tokens were published unintentionally and the software has been made public, you should revoke exposed tokens and file a security incident.

Examples of service tokens that may have been detected include AWS, Facebook, JWT, SWT, Slack and others.

Problem

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

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.

Problem

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.

Problem

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

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 using components that are rarely used to build applications 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

11 packages

found in

Top 1k

910 packages

found in

Top 10k

717.09k 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.

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.

codefrequencychecker

SAFE Assessment

Compliance

Security

Threats

IMPORTANT: All versions of this package have been maliciousLatest version with malware incident detected: about 2 months agoVersion: 0.0.7

INCIDENTS FOR THIS VERSION:

SQ30105Detected presence of known software supply chain attack artifacts.Causes risk: supply chain attack artifactsthreats

Problem

Prevalence in PyPI community

Next steps

SQ30109Detected presence of malicious files through analyst-vetted file reputation.Causes risk: analyst-vetted malware foundthreats

Problem

Prevalence in PyPI community

Next steps

SQ34401Detected presence of active web service tokens.Causes risk: active web service credentialssecrets

Problem

Prevalence in PyPI community

Next steps

TH15501Detected presence of files with behaviors similar to malicious packages published on PyPI.Causes risk: suspicious application behaviorshunting

Problem

Prevalence in PyPI community

Next steps

TH30105Detected presence of software components that were recently published to the public package repository.hunting

Problem

Prevalence in PyPI community

Next steps

TH30106Detected presence of software components that have low popularity or number of downloads.hunting

Problem

Prevalence in PyPI community

Next steps

TH15103Detected presence of files with behaviors commonly used by malicious software.hunting

Problem

Prevalence in PyPI community

Next steps

TH30107Detected presence of software components that are rarely included by other public software packages.hunting

Problem

Prevalence in PyPI community

Next steps

TH30112Detected presence of software components without a declared source code repository.hunting

Problem

Prevalence in PyPI community

Next steps

Did you know...

SQ30105Detected presence of known software supply chain attack artifacts.Causes risk: supply chain attack artifactsthreats

Problem

Prevalence in PyPI community

Next steps

SQ30109Detected presence of malicious files through analyst-vetted file reputation.Causes risk: analyst-vetted malware foundthreats

Problem

Prevalence in PyPI community

Next steps

SQ34401Detected presence of active web service tokens.Causes risk: active web service credentialssecrets

Problem

Prevalence in PyPI community

Next steps

TH15501Detected presence of files with behaviors similar to malicious packages published on PyPI.Causes risk: suspicious application behaviorshunting

Problem

Prevalence in PyPI community

Next steps

TH30105Detected presence of software components that were recently published to the public package repository.hunting

Problem

Prevalence in PyPI community

Next steps

TH30106Detected presence of software components that have low popularity or number of downloads.hunting

Problem

Prevalence in PyPI community

Next steps

TH15103Detected presence of files with behaviors commonly used by malicious software.hunting

Problem

Prevalence in PyPI community

Next steps

TH30107Detected presence of software components that are rarely included by other public software packages.hunting

Problem

Prevalence in PyPI community

Next steps

TH30112Detected presence of software components without a declared source code repository.hunting

Problem

Prevalence in PyPI community

Next steps