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failIncident: Malware
Scanned: 2 days ago

requestet-loader

Artifact:
latest
malicious
Research
Load and run code from Pastebin using base64 ID
License: unknown
Published: 8 days ago
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 suspicious application behaviors
Malware
3 analyst-vetted malware found

INCIDENTS:

malware
8 days agoReported By: ReversingLabs (Researcher)
Learn more about malware detection

Popularity

293
Total Downloads
1
Contributor
1
Declared Dependencies
0
Dependents

Top issues

See all issues

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.

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.

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.

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.

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 using components that are rarely used to build applications 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.

Top behaviors

See all behaviors

Prevalence in PyPI community

Behavior often found in this community (Common)
40 packages
found in
Top 100
253 packages
found in
Top 1k
1433 packages
found in
Top 10k
53416 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
54 packages
found in
Top 100
343 packages
found in
Top 1k
1872 packages
found in
Top 10k
49508 packages
in community

Prevalence in PyPI community

No behavior prevalence information at this time

Prevalence in PyPI community

Behavior often found in this community (Common)
68 packages
found in
Top 100
585 packages
found in
Top 1k
4048 packages
found in
Top 10k
149289 packages
in community

Prevalence in PyPI community

Behavior uncommon for this community (Uncommon)
1 packages
found in
Top 100
17 packages
found in
Top 1k
104 packages
found in
Top 10k
3113 packages
in community

Top vulnerabilities

No vulnerabilities found.

FAQ for requestet-loader

What is requestet-loader?

Load and run code from Pastebin using base64 ID.

Is requestet-loader malicious or is it safe to use?

The Python package requestet-loader 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 requestet-loader package were malicious.

Is requestet-loader popular?

The Python package requestet-loader is not widely used. It has 293 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 requestet-loader once it is in my app?

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