Spectra Assure

Quarantine

Scanned:

mlflow-ui

Artifact:

latest

malicious

Standalone UI helpers for MLflow tracking servers

License: Permissive (Apache-2.0)

New!

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

1 quarantined components found

Malware

1 malicious components found

INCIDENTS FOR THIS VERSION:

malware

Reported By: ReversingLabs (Automated)

Learn more about malware detection

Overview issues9 vulnerabilities0 malware1 behaviors28 dependencies0 contributors1 versions3

Popularity

434

Total Downloads

Contributor

Declared Dependencies

Dependents

Top issues

See all issues

Problem

Third-party malware detection algorithms have determined that the software package contains one or more malicious files. The detection was made by a hash-based file reputation lookup. This malware detection method is considered accurate, and can typically identify the malware family by name.

Prevalence in PyPI community

1 packages

found in

Top 100

0 packages

found in

Top 1k

19 packages

found in

Top 10k

772 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

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. The ReversingLabs community package index may place software packages into quarantine during an ongoing security incident investigation. When a software project is placed into quarantine, it is typically unavailable for installation. Additionally, repository maintainers may also hide quarantined software projects from users that might stumble upon them using repository search functions. Quarantine is typically raised for software projects that were reported for unusual code changes or unexpected software behaviors.

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 they are well-documented.

Avoid using this software package until it is vetted as safe.

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

Applications communicate with web services by exchanging HTTP requests. During software development, externally hosted services are used by developers to debug software quality issues relating to exchanging HTTP requests. Attackers commonly abuse tools designed for HTTP request inspection to monitor network traffic and extract sensitive information from the HTTP traffic. While the presence of domains related to HTTP inspection does not imply malicious intent, all of their uses in a software package should be documented and approved. Attackers might have purposely injected security testing tools in the software package to monitor the network traffic of the infected computer system. It is also possible that the software package has mistakenly included a part of its testing infrastructure during packaging.

Prevalence in PyPI community

11 packages

found in

Top 100

41 packages

found in

Top 1k

239 packages

found in

Top 10k

11.68k 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 removing all references to flagged network locations.

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. Some open source projects have a history of security lapses that culminated with a publication of one or more malicious component versions. To ensure that repeated supply chain incidents do not occur, the open source project should be closely monitored for up to two years. All software package versions that are published within two years of the malware incident will convey a warning about the history of security incidents tied to the open source project.

Prevalence in PyPI community

2 packages

found in

Top 100

18 packages

found in

Top 1k

110 packages

found in

Top 10k

1.99k 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

See all behaviors

Prevalence in PyPI community

Behavior often found in this community (Common)

36 packages

found in

Top 100

281 packages

found in

Top 1k

1802 packages

found in

Top 10k

66.4k packages

in community

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

Prevalence in PyPI community

No behavior prevalence information at this time

Prevalence in PyPI community

No behavior prevalence information at this time

Prevalence in PyPI community

No behavior prevalence information at this time

Top vulnerabilities

No vulnerabilities found.

FAQ for mlflow-ui

What is mlflow-ui?

Standalone UI helpers for MLflow tracking servers.

Is mlflow-ui malicious or is it safe to use?

The Python package mlflow-ui was scanned for malware, software tampering, risky behaviors, exposed secrets and known vulnerabilities. Malware or tampering has been detected, making it unsafe to download and use. 1 other issue and risky behavior was found. The package contains risks, and the analysis results should be reviewed before it is downloaded and used. Visit the Issues and Behaviors sections of the analysis for more details. All versions of the mlflow-ui package were malicious.

Is mlflow-ui popular?

The Python package mlflow-ui is not widely used. It has 434 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 mlflow-ui once it is in my app?

Since we can't know when or where malicious attacks will happen, we recommend tracking how mlflow-ui behaviors change over multiple software releases. By adopting differential analysis in your release process, you can detect unexpected changes to a mlflow-ui 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.

This website uses cookies to ensure the best website experience. By continuing to use this website you are giving your consent to cookies being used. Detailed information about our use of cookies is here.

Popularity

434

Total Downloads

Contributor

Declared Dependencies

Dependents

Top issues

See all issues

Problem

Prevalence in PyPI community

1 packages

found in

Top 100

0 packages

found in

Top 1k

19 packages

found in

Top 10k

772 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

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 they are well-documented.

Avoid using this software package until it is vetted as safe.

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

11 packages

found in

Top 100

41 packages

found in

Top 1k

239 packages

found in

Top 10k

11.68k 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 removing all references to flagged network locations.

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. Some open source projects have a history of security lapses that culminated with a publication of one or more malicious component versions. To ensure that repeated supply chain incidents do not occur, the open source project should be closely monitored for up to two years. All software package versions that are published within two years of the malware incident will convey a warning about the history of security incidents tied to the open source project.

Prevalence in PyPI community

2 packages

found in

Top 100

18 packages

found in

Top 1k

110 packages

found in

Top 10k

1.99k 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

See all behaviors

Prevalence in PyPI community

Behavior often found in this community (Common)

36 packages

found in

Top 100

281 packages

found in

Top 1k

1802 packages

found in

Top 10k

66.4k packages

in community

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

Prevalence in PyPI community

No behavior prevalence information at this time

Prevalence in PyPI community

No behavior prevalence information at this time

Prevalence in PyPI community

No behavior prevalence information at this time

Top vulnerabilities

No vulnerabilities found.

mlflow-ui

SAFE Assessment

Compliance

Security

Threats

IMPORTANT: All versions of this package have been maliciousLatest version with malware incident detected: 2 days agoVersion: 2.7.3

INCIDENTS FOR THIS VERSION:

Popularity

Top issues

SQ30110Detected presence of malicious files through file reputation or third-party scanners.Causes risk: malicious components foundthreats

Problem

Prevalence in PyPI community

Next steps

TH30102Detected presence of software components that are quarantined in the community package index.Causes risk: quarantined components 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

TH17131Detected presence of files containing domains used for intercepting and inspecting HTTP requests.hunting

Problem

Prevalence in PyPI community

Next steps

TH30101Detected presence of software components that had a recent malware or tampering incident.Causes risk: components with malware historyhunting

Problem

Prevalence in PyPI community

Next steps

Top behaviors

Encodes data using the Base64 algorithm.packer

Prevalence in PyPI community

Contains URLs that link to interesting file formats.network

Prevalence in PyPI community

Contains URLs related to the AWS Instance Metadata Service (IMDS) token acquisition endpoint.network

Prevalence in PyPI community

Contains URLs related to the AWS Instance Metadata Service (IMDS) endpoint.network

Prevalence in PyPI community

Contains URLs related to the AWS ECS Task Metadata endpoint.network

Prevalence in PyPI community

Top vulnerabilities

FAQ for mlflow-ui

What is mlflow-ui?

Is mlflow-ui malicious or is it safe to use?

Is mlflow-ui popular?

How do I secure mlflow-ui once it is in my app?

Did you know...

Popularity

Top issues

SQ30110Detected presence of malicious files through file reputation or third-party scanners.Causes risk: malicious components foundthreats

Problem

Prevalence in PyPI community

Next steps

TH30102Detected presence of software components that are quarantined in the community package index.Causes risk: quarantined components 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

TH17131Detected presence of files containing domains used for intercepting and inspecting HTTP requests.hunting

Problem

Prevalence in PyPI community

Next steps

TH30101Detected presence of software components that had a recent malware or tampering incident.Causes risk: components with malware historyhunting

Problem

Prevalence in PyPI community

Next steps

Top behaviors

Encodes data using the Base64 algorithm.packer

Prevalence in PyPI community

Contains URLs that link to interesting file formats.network

Prevalence in PyPI community

Contains URLs related to the AWS Instance Metadata Service (IMDS) token acquisition endpoint.network

Prevalence in PyPI community

Contains URLs related to the AWS Instance Metadata Service (IMDS) endpoint.network

Prevalence in PyPI community

Contains URLs related to the AWS ECS Task Metadata endpoint.network

Prevalence in PyPI community