Spectra Assure

Quarantine

Scanned:

quant-backtest-helpers

Artifact:

latest

Helper utilities for quantitative backtesting workflows

License: unknown

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

No evidence of malware inclusion

Overview issues8 vulnerabilities0 malware0 behaviors14 dependencies0 contributors1 versions1

Popularity

268

Total Downloads

Contributor

Declared Dependencies

Dependents

Top issues

See all issues

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

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

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

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.

Top behaviors

See all behaviors

Prevalence in PyPI community

Behavior commonly used by malicious software (Important)

Behavior uncommon for this community (Uncommon)

2 packages

found in

Top 100

12 packages

found in

Top 1k

117 packages

found in

Top 10k

17.03k packages

in community

Prevalence in PyPI community

Behavior often found in this community (Common)

33 packages

found in

Top 100

151 packages

found in

Top 1k

577 packages

found in

Top 10k

10.84k packages

in community

Prevalence in PyPI community

Behavior often found in this community (Common)

51 packages

found in

Top 100

408 packages

found in

Top 1k

2829 packages

found in

Top 10k

106.17k packages

in community

Prevalence in PyPI community

Behavior often found in this community (Common)

30 packages

found in

Top 100

166 packages

found in

Top 1k

1155 packages

found in

Top 10k

31.66k packages

in community

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

Top vulnerabilities

No vulnerabilities found.

FAQ for quant-backtest-helpers

What is quant-backtest-helpers?

Helper utilities for quantitative backtesting workflows.

Is quant-backtest-helpers malicious or is it safe to use?

The Python package quant-backtest-helpers 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. 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.

Is quant-backtest-helpers popular?

The Python package quant-backtest-helpers is not widely used. It has 268 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 quant-backtest-helpers once it is in my app?

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

268

Total Downloads

Contributor

Declared Dependencies

Dependents

Top issues

See all issues

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

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

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

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.

Top behaviors

See all behaviors

Prevalence in PyPI community

Behavior commonly used by malicious software (Important)

Behavior uncommon for this community (Uncommon)

2 packages

found in

Top 100

12 packages

found in

Top 1k

117 packages

found in

Top 10k

17.03k packages

in community

Prevalence in PyPI community

Behavior often found in this community (Common)

33 packages

found in

Top 100

151 packages

found in

Top 1k

577 packages

found in

Top 10k

10.84k packages

in community

Prevalence in PyPI community

Behavior often found in this community (Common)

51 packages

found in

Top 100

408 packages

found in

Top 1k

2829 packages

found in

Top 10k

106.17k packages

in community

Prevalence in PyPI community

Behavior often found in this community (Common)

30 packages

found in

Top 100

166 packages

found in

Top 1k

1155 packages

found in

Top 10k

31.66k packages

in community

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

Top vulnerabilities

No vulnerabilities found.

quant-backtest-helpers

SAFE Assessment

Compliance

Security

Threats

Popularity

Top issues

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

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

Top behaviors

The software package was developed by a user registered with an anonymous email address.anomaly

Prevalence in PyPI community

Decodes hex or base64-encoded streams.packer

Prevalence in PyPI community

Contains unusually long strings.anomaly

Prevalence in PyPI community

Opens a URL.network

Prevalence in PyPI community

The software package does not declare any source code repository.anomaly

Prevalence in PyPI community

Top vulnerabilities

FAQ for quant-backtest-helpers

What is quant-backtest-helpers?

Is quant-backtest-helpers malicious or is it safe to use?

Is quant-backtest-helpers popular?

How do I secure quant-backtest-helpers once it is in my app?

Did you know...

Popularity

Top issues

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

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

Top behaviors

The software package was developed by a user registered with an anonymous email address.anomaly

Prevalence in PyPI community

Decodes hex or base64-encoded streams.packer

Prevalence in PyPI community

Contains unusually long strings.anomaly

Prevalence in PyPI community

Opens a URL.network

Prevalence in PyPI community

The software package does not declare any source code repository.anomaly

Prevalence in PyPI community

Top vulnerabilities