Spectra Assure
Community
failRisk: Vulnerabilities
Scanned: about 11 hours ago

polars-runtime-32

Artifact:
latest
Top 10k
Blazingly fast DataFrame library
License: Permissive (MIT)
Published: 16 days ago
Visit GitHub
See on PyPI

SAFE Assessment

Compliance

Licenses
No license compliance issues
Secrets
No sensitive information found

Security

Vulnerabilities
2 severe vulnerabilities exploited
Hardening
3 misconfigured toolchains detected

Threats

Tampering
No evidence of software tampering
Malware
No evidence of malware inclusion

INCIDENTS FOR THIS VERSION:

Popularity

47.79M
Total Downloads
0
Contributors
0
Declared Dependencies
2
Dependents

Top issues

See all issues

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

35 packages
found in
Top 100
210 packages
found in
Top 1k
1787 packages
found in
Top 10k
86.39k 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.

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 critical severity.

Prevalence in PyPI community

27 packages
found in
Top 100
156 packages
found in
Top 1k
1294 packages
found in
Top 10k
58.62k packages
in community

Next steps

Perform impact analysis for the reported CVEs.
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.

Problem

Security Development Lifecycle (SDL) is a group of enhanced compile-time checks that report common coding mistakes as errors. These checks prevent the use of hard-to-secure string manipulation functions. They enforce static memory access checks, and allow only the use of range-verified string parsing functions. While these checks do not prevent every memory corruption issue by themselves, they do help reduce the likelihood.

Prevalence in PyPI community

10 packages
found in
Top 100
45 packages
found in
Top 1k
247 packages
found in
Top 10k
4.13k packages
in community

Next steps

It's highly recommended to enable these checks for all software components used at security boundaries, or those that process user controlled inputs.
To enable these checks, refer to your programming language toolchain documentation.
In Microsoft VisualStudio, you can enable this feature by setting the compiler option /SDL to ON.

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. Some open source repositories allow the developers to take down software component versions that they have published. For open source projects, version unpublishing is uncommon. Versions are typically removed due to a security incident, such as malicious code tampering or accidental development secrets exposure. Software developers often prioritize taking down such packages before informing the community that they have experienced a security incident. Therefore, it is prudent to review the reasons behind software version removals as these events might be a signal of an ongoing software supply chain attack.

Prevalence in PyPI community

47 packages
found in
Top 100
238 packages
found in
Top 1k
1834 packages
found in
Top 10k
89.18k packages
in community

Next steps

Review software component documentation for the reasons behind the recent version removal.
If the software version was removed due to a security incident, 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)
14 packages
found in
Top 100
98 packages
found in
Top 1k
585 packages
found in
Top 10k
18.68k packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
10 packages
found in
Top 100
93 packages
found in
Top 1k
490 packages
found in
Top 10k
15.62k packages
in community

Prevalence in PyPI community

Behavior uncommon for this community (Uncommon)
2 packages
found in
Top 100
23 packages
found in
Top 1k
147 packages
found in
Top 10k
2.59k packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
27 packages
found in
Top 100
124 packages
found in
Top 1k
702 packages
found in
Top 10k
11.25k packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
17 packages
found in
Top 100
126 packages
found in
Top 1k
796 packages
found in
Top 10k
25.62k packages
in community

Top vulnerabilities

See all vulnerabilities
Vulnerability Exploitation Lifecycle
(2 Active Vulnerabilities)
None
2 (2 Fixable)
CVE-2022-37434c
CVE-2018-25032h
None
None
Exploits Unknown
Exploits Exist
Exploited by Malware
Patching Mandated

FAQ for polars-runtime-32

What is polars-runtime-32?

Blazingly fast DataFrame library.

Is polars-runtime-32 malicious or is it safe to use?

The Python package polars-runtime-32 was scanned for malware, software tampering, risky behaviors, exposed secrets and known vulnerabilities. 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 polars-runtime-32 popular?

The Python package polars-runtime-32 ranks among the top 10000 projects in this community. It has 48M 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 polars-runtime-32 once it is in my app?

Since we can't know when or where malicious attacks will happen, we recommend tracking how polars-runtime-32 behaviors change over multiple software releases. By adopting differential analysis in your release process, you can detect unexpected changes to a polars-runtime-32 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.