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failIncident: Malware

Scanned:

prof-tgqu

Artifact:

latest

removed

malicious

Research

A trading bot

License: Permissive (MIT)

Published:

See on PyPI

SAFE Assessment

Compliance

Licenses

No license compliance issues

Secrets

No sensitive information found

Security

Vulnerabilities

1 high severity vulnerabilities

Hardening

No application hardening issues

Threats

Tampering

1 components prone to hijacking

Malware

3 supply chain attack artifacts

INCIDENTS FOR THIS VERSION:

removal

Reported By: Community

malware

Reported By: Community (OpenSSF)

More info

malware

Reported By: ReversingLabs (Researcher)

Learn more about malware detection

malware

Reported By: Community (VirusTotal)

More info

malware

Reported By: Community (GitHub Advisory)

More info

malware

N/AReported By: Community (kam193)

More info

Overview issues7 vulnerabilities1 malware3 behaviors5 dependencies8 contributors0 versions1

Popularity

144

Total Downloads

Contributors

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 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

42 packages

found in

Top 100

278 packages

found in

Top 1k

1960 packages

found in

Top 10k

77.08k 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.

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

0 packages

found in

Top 100

1 packages

found in

Top 1k

20 packages

found in

Top 10k

83.87k 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.

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.

Top behaviors

See all behaviors

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

Prevalence in PyPI community

Behavior often found in this community (Common)

20 packages

found in

Top 100

182 packages

found in

Top 1k

1802 packages

found in

Top 10k

186.71k 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

Prevalence in PyPI community

Behavior often found in this community (Common)

10 packages

found in

Top 100

79 packages

found in

Top 1k

571 packages

found in

Top 10k

24.1k packages

in community

Prevalence in PyPI community

Behavior often found in this community (Common)

95 packages

found in

Top 100

837 packages

found in

Top 1k

7695 packages

found in

Top 10k

495.5k packages

in community

Top vulnerabilities

See all vulnerabilities

Vulnerability Exploitation Lifecycle

(1 Active Vulnerabilities)

1 (1 Fixable)

CVE-2024-39689h

None

Exploits Unknown

Exploits Exist

Exploited by Malware

Patching Mandated

FAQ for prof-tgqu

What is prof-tgqu?

A trading bot.

Is prof-tgqu malicious or is it safe to use?

The Python package prof-tgqu 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 prof-tgqu package were malicious.

Is prof-tgqu popular?

The Python package prof-tgqu is not widely used. It has 144 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 prof-tgqu once it is in my app?

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

144

Total Downloads

Contributors

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

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

42 packages

found in

Top 100

278 packages

found in

Top 1k

1960 packages

found in

Top 10k

77.08k 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.

Problem

Prevalence in PyPI community

0 packages

found in

Top 100

1 packages

found in

Top 1k

20 packages

found in

Top 10k

83.87k 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.

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.

Top behaviors

See all behaviors

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

Prevalence in PyPI community

Behavior often found in this community (Common)

20 packages

found in

Top 100

182 packages

found in

Top 1k

1802 packages

found in

Top 10k

186.71k 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

Prevalence in PyPI community

Behavior often found in this community (Common)

10 packages

found in

Top 100

79 packages

found in

Top 1k

571 packages

found in

Top 10k

24.1k packages

in community

Prevalence in PyPI community

Behavior often found in this community (Common)

95 packages

found in

Top 100

837 packages

found in

Top 1k

7695 packages

found in

Top 10k

495.5k packages

in community

Top vulnerabilities

See all vulnerabilities

Vulnerability Exploitation Lifecycle

(1 Active Vulnerabilities)

1 (1 Fixable)

CVE-2024-39689h

None

Exploits Unknown

Exploits Exist

Exploited by Malware

Patching Mandated

prof-tgqu

SAFE Assessment

Compliance

Security

Threats

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

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

SQ31105Detected presence of high severity vulnerabilities.Causes risk: high severity vulnerabilitiesvulnerabilities

Problem

Prevalence in PyPI community

Next steps

TH30103Detected presence of software components that were removed from the public package repository.Causes risk: components prone to hijackinghunting

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

Top behaviors

Executes an expression.execution

Prevalence in PyPI community

Creates shortcuts during installation or upon launch.file

Prevalence in PyPI community

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

Prevalence in PyPI community

Contains patterns identifying the constants related to the base64 binary-to-text encoding algorithm.signature

Prevalence in PyPI community

Creates/Opens a file.file

Prevalence in PyPI community

Top vulnerabilities

FAQ for prof-tgqu

What is prof-tgqu?

Is prof-tgqu malicious or is it safe to use?

Is prof-tgqu popular?

How do I secure prof-tgqu 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

SQ31105Detected presence of high severity vulnerabilities.Causes risk: high severity vulnerabilitiesvulnerabilities

Problem

Prevalence in PyPI community

Next steps

TH30103Detected presence of software components that were removed from the public package repository.Causes risk: components prone to hijackinghunting

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

Top behaviors

Executes an expression.execution

Prevalence in PyPI community

Creates shortcuts during installation or upon launch.file

Prevalence in PyPI community

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

Prevalence in PyPI community

Contains patterns identifying the constants related to the base64 binary-to-text encoding algorithm.signature

Prevalence in PyPI community

Creates/Opens a file.file

Prevalence in PyPI community