failIncident: Malware

Scanned:

audio-separator-fork

Artifact:

latest

Research

Easy to use vocal separation on CLI or as a python package, using the amazing MDX-Net models from UVR trained by @Anjok07

License: Permissive (MIT)

Published:

See on PyPI

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

2 malware-like behaviors found

Malware

3 supply chain attack artifacts

INCIDENTS FOR THIS VERSION:

malware

Reported By: ReversingLabs (Researcher)

Learn more about malware detection

malware

Reported By: Community (OpenSSF)

More info

removal

Reported By: Community

malware

N/AReported By: Community (kam193)

More info

Popularity

6.05k

Total Downloads

Contributor

Declared Dependencies

Dependents

Top issues

See all issues

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 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 backdoor malware profile. Backdoors are commonly used by malicious actors to gain unauthorized access to exposed computer systems over the internet. However, due to high-privilege access requirements, some security solutions may also trigger this detection when analyzed.

Prevalence in PyPI community

0 packages

found in

Top 100

0 packages

found in

Top 1k

1 packages

found in

Top 10k

192 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.

In the case this behavior is intended, rewrite the flagged code without using the malware-like behaviors.

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.

Top behaviors

See all behaviors

Prevalence in PyPI community

Behavior often found in this community (Common)

17 packages

found in

Top 100

91 packages

found in

Top 1k

572 packages

found in

Top 10k

16.23k packages

in community

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

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

Prevalence in PyPI community

Behavior often found in this community (Common)

22 packages

found in

Top 100

83 packages

found in

Top 1k

430 packages

found in

Top 10k

9.5k packages

in community

Prevalence in PyPI community

Behavior often found in this community (Common)

53 packages

found in

Top 100

351 packages

found in

Top 1k

1856 packages

found in

Top 10k

48.82k packages

in community

Top vulnerabilities

No vulnerabilities found.

FAQ for audio-separator-fork

What is audio-separator-fork?

Easy to use vocal separation on CLI or as a python package, using the amazing MDX-Net models from UVR trained by @Anjok07. For more information about this package, visit its homepage.

Is audio-separator-fork malicious or is it safe to use?

The Python package audio-separator-fork has been removed from registry. It was scanned for malware, software tampering, risky behaviors, exposed secrets and known vulnerabilities. Malware or tampering has been detected, making it unsafe to use. 2 other issues and risky behaviors were found. The package contains risks, and the analysis results should be reviewed before it is used. Visit the Issues and Behaviors sections of the analysis for more details. All versions of the audio-separator-fork package were malicious.

Is audio-separator-fork popular?

The Python package audio-separator-fork is not widely used. It has 6K recorded downloads. A package's popularity is not a good indicator of its safety, visit the SAFE Assessment section to see the full analysis of package deployment risk categories.

How do I secure audio-separator-fork once it is in my app?

Since we can't know when or where malicious attacks will happen, we recommend tracking how audio-separator-fork behaviors change over multiple software releases. By adopting differential analysis in your release process, you can detect unexpected changes to a audio-separator-fork version, which can prevent advanced software supply chain attacks. Read how our technology can help prevent future attacks similar to SolarWinds and 3CX.

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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Popularity

6.05k

Total Downloads

Contributor

Declared Dependencies

Dependents

Top issues

See all issues

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

1 packages

found in

Top 10k

192 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.

In the case this behavior is intended, rewrite the flagged code without using the malware-like behaviors.

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

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.

Top behaviors

See all behaviors

Prevalence in PyPI community

Behavior often found in this community (Common)

17 packages

found in

Top 100

91 packages

found in

Top 1k

572 packages

found in

Top 10k

16.23k packages

in community

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

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

Prevalence in PyPI community

Behavior often found in this community (Common)

22 packages

found in

Top 100

83 packages

found in

Top 1k

430 packages

found in

Top 10k

9.5k packages

in community

Prevalence in PyPI community

Behavior often found in this community (Common)

53 packages

found in

Top 100

351 packages

found in

Top 1k

1856 packages

found in

Top 10k

48.82k packages

in community

Top vulnerabilities

No vulnerabilities found.

audio-separator-fork

SAFE Assessment

Compliance

Security

Threats

IMPORTANT: All versions of this package have been maliciousLatest version with malware incident detected: over 1 year agoVersion: 0.8.11

INCIDENTS FOR THIS VERSION:

Popularity

Top issues

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

TH15106Detected presence of files with behaviors that match the backdoor malware profile.Causes risk: malware-like behaviors foundhunting

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

TH17127Detected presence of files containing URLs that link to raw files on GitHub.hunting

Problem

Prevalence in PyPI community

Next steps

Top behaviors

Downloads a file.network

Prevalence in PyPI community

Decodes data using the Base64 algorithm.packer

Prevalence in PyPI community

Contains URLs that link to raw files on GitHub.network

Prevalence in PyPI community

Decompresses data using the Zlib algorithm.packer

Prevalence in PyPI community

Executes an expression.execution

Prevalence in PyPI community

Top vulnerabilities

FAQ for audio-separator-fork

What is audio-separator-fork?

Is audio-separator-fork malicious or is it safe to use?

Is audio-separator-fork popular?

How do I secure audio-separator-fork once it is in my app?

Did you know...

Popularity

Top issues

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

TH15106Detected presence of files with behaviors that match the backdoor malware profile.Causes risk: malware-like behaviors foundhunting

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

TH17127Detected presence of files containing URLs that link to raw files on GitHub.hunting

Problem

Prevalence in PyPI community

Next steps

Top behaviors

Downloads a file.network

Prevalence in PyPI community

Decodes data using the Base64 algorithm.packer

Prevalence in PyPI community

Contains URLs that link to raw files on GitHub.network

Prevalence in PyPI community

Decompresses data using the Zlib algorithm.packer

Prevalence in PyPI community

Executes an expression.execution

Prevalence in PyPI community