Spectra Assure

failIncident: Malware

Scanned:

CryptoAiTools

Artifact:

latest

removed

malicious

Research

A Python toolkit to create and manage crypto trading bots

License: unknown

Published:

See on PyPI

SAFE Assessment

Compliance

Licenses

No license compliance issues

Secrets

No sensitive information found

Security

Vulnerabilities

1 patch mandated vulnerabilities

Hardening

No application hardening issues

Threats

Tampering

1 suspicious application behaviors

Malware

4 supply chain attack artifacts

IMPORTANT: All versions of this package have been malicious

Latest malware incident detected:

Version: 0.7

INCIDENTS:

malware

Reported By: ReversingLabs (Researcher)

Learn more about malware detection

malware

Reported By: Community (Checkmarx)

More info

malware

Reported By: Community (Snyk)

More info

removal

Reported By: Community

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

Policies

Info

Count

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

5 packages

found in

Top 10k

13913 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.

SQ30109

Detected presence of malicious files through analyst-vetted file reputation.

Causes risk: analyst-vetted malware found

threats

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

7 packages

found in

Top 10k

13959 packages

in community

Next steps

Investigate the build and release environment for software supply chain compromise.

Avoid using this software package.

SQ31101

Detected presence of patch mandated vulnerabilities.

Causes risk: patch mandated vulnerabilities

vulnerabilities

Problem

Software composition analysis has identified a component with one or more known vulnerabilities. Available threat intelligence telemetry has confirmed that the reported vulnerabilities are actively being exploited by malicious actors. Regulatory body or a government agency has issued a patching mandate for all software components affected by the identified vulnerabilities.

Prevalence in PyPI community

1 packages

found in

Top 100

24 packages

found in

Top 1k

213 packages

found in

Top 10k

9715 packages

in community

Next steps

We strongly advise updating the component to the latest version.

If the update can't resolve the issue, create a plan to isolate or replace the affected component.

SQ31102

Detected presence of severe vulnerabilities with active exploitation.

Causes risk: actively exploited vulnerabilities

vulnerabilities

Problem

Software composition analysis has identified a component with one or more known severe vulnerabilities. Available threat intelligence telemetry has confirmed that the reported high or critical severity vulnerabilities are actively being exploited by malicious actors.

Prevalence in PyPI community

38 packages

found in

Top 100

303 packages

found in

Top 1k

2611 packages

found in

Top 10k

103184 packages

in community

Next steps

We strongly advise updating the component to the latest version.

If the update can't resolve the issue, create a plan to isolate or replace the affected component.

SQ31103

Detected presence of malware-exploited vulnerabilities.

Causes risk: malware exploited vulnerabilities

vulnerabilities

Problem

Software composition analysis has identified a component with one or more known vulnerabilities. Available threat intelligence telemetry has confirmed that the reported vulnerabilities are actively being exploited by malicious actors. Malware code that propagates through these vulnerabilities has been created. This increases the chance of automated malware attacks affecting the software component users.

Prevalence in PyPI community

10 packages

found in

Top 100

78 packages

found in

Top 1k

751 packages

found in

Top 10k

27887 packages

in community

Next steps

We strongly advise updating the component to the latest version.

If the update can't resolve the issue, create a plan to isolate or replace the affected component.

SQ31105

Detected presence of high severity vulnerabilities.

Causes risk: high severity vulnerabilities

vulnerabilities

Problem

Software composition analysis has identified a component with one or more known vulnerabilities. Based on the CVSS scoring, these vulnerabilities have been marked as high severity.

Prevalence in PyPI community

50 packages

found in

Top 100

352 packages

found in

Top 1k

2858 packages

found in

Top 10k

108771 packages

in community

Next steps

Perform impact analysis for the reported CVEs.

Update the component to the latest version.

If the update can't resolve the issue, create a plan to isolate or replace the affected component.

TH15501

Detected presence of files with behaviors similar to malicious packages published on PyPI.

Causes risk: suspicious application behaviors

hunting

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.

TH30103

Detected presence of software components that were removed from the public package repository.

Causes risk: components prone to hijacking

hunting

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.

TH30106

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

hunting

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.

SQ31106

Detected presence of medium severity vulnerabilities.

Causes risk: medium severity vulnerabilities

vulnerabilities

Problem

Software composition analysis has identified a component with one or more known vulnerabilities. Based on the CVSS scoring, these vulnerabilities have been marked as medium severity.

Prevalence in PyPI community

48 packages

found in

Top 100

311 packages

found in

Top 1k

2806 packages

found in

Top 10k

142146 packages

in community

Next steps

Perform impact analysis for the reported CVEs.

Update the component to the latest version.

If the update can't resolve the issue, create a plan to isolate or replace the affected component.

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.

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

Policies

Info

Count

Problem

Prevalence in PyPI community

0 packages

found in

Top 100

0 packages

found in

Top 1k

5 packages

found in

Top 10k

13913 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.

SQ30109

Detected presence of malicious files through analyst-vetted file reputation.

Causes risk: analyst-vetted malware found

threats

Problem

Prevalence in PyPI community

0 packages

found in

Top 100

0 packages

found in

Top 1k

7 packages

found in

Top 10k

13959 packages

in community

Next steps

Investigate the build and release environment for software supply chain compromise.

Avoid using this software package.

SQ31101

Detected presence of patch mandated vulnerabilities.

Causes risk: patch mandated vulnerabilities

vulnerabilities

Problem

Prevalence in PyPI community

1 packages

found in

Top 100

24 packages

found in

Top 1k

213 packages

found in

Top 10k

9715 packages

in community

Next steps

We strongly advise updating the component to the latest version.

If the update can't resolve the issue, create a plan to isolate or replace the affected component.

SQ31102

Detected presence of severe vulnerabilities with active exploitation.

Causes risk: actively exploited vulnerabilities

vulnerabilities

Problem

Prevalence in PyPI community

38 packages

found in

Top 100

303 packages

found in

Top 1k

2611 packages

found in

Top 10k

103184 packages

in community

Next steps

We strongly advise updating the component to the latest version.

If the update can't resolve the issue, create a plan to isolate or replace the affected component.

SQ31103

Detected presence of malware-exploited vulnerabilities.

Causes risk: malware exploited vulnerabilities

vulnerabilities

Problem

Software composition analysis has identified a component with one or more known vulnerabilities. Available threat intelligence telemetry has confirmed that the reported vulnerabilities are actively being exploited by malicious actors. Malware code that propagates through these vulnerabilities has been created. This increases the chance of automated malware attacks affecting the software component users.

Prevalence in PyPI community

10 packages

found in

Top 100

78 packages

found in

Top 1k

751 packages

found in

Top 10k

27887 packages

in community

Next steps

We strongly advise updating the component to the latest version.

If the update can't resolve the issue, create a plan to isolate or replace the affected component.

SQ31105

Detected presence of high severity vulnerabilities.

Causes risk: high severity vulnerabilities

vulnerabilities

Problem

Software composition analysis has identified a component with one or more known vulnerabilities. Based on the CVSS scoring, these vulnerabilities have been marked as high severity.

Prevalence in PyPI community

50 packages

found in

Top 100

352 packages

found in

Top 1k

2858 packages

found in

Top 10k

108771 packages

in community

Next steps

Perform impact analysis for the reported CVEs.

Update the component to the latest version.

If the update can't resolve the issue, create a plan to isolate or replace the affected component.

TH15501

Detected presence of files with behaviors similar to malicious packages published on PyPI.

Causes risk: suspicious application behaviors

hunting

Problem

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.

TH30103

Detected presence of software components that were removed from the public package repository.

Causes risk: components prone to hijacking

hunting

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 that their versions are pinned.

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

TH30106

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

hunting

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.

SQ31106

Detected presence of medium severity vulnerabilities.

Causes risk: medium severity vulnerabilities

vulnerabilities

Problem

Software composition analysis has identified a component with one or more known vulnerabilities. Based on the CVSS scoring, these vulnerabilities have been marked as medium severity.

Prevalence in PyPI community

48 packages

found in

Top 100

311 packages

found in

Top 1k

2806 packages

found in

Top 10k

142146 packages

in community

Next steps

Perform impact analysis for the reported CVEs.

Update the component to the latest version.

If the update can't resolve the issue, create a plan to isolate or replace the affected component.