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failIncident: Malware
Scanned: 3 days ago

ai-labs-snippets-sdk

Artifact:
latest
malicious
AI Labs Snippets SDK for Python
License: Permissive (MIT)
Published: 7 months ago
See on PyPI

SAFE Assessment

Compliance

Licenses
No license compliance issues
Secrets
No sensitive information found

Security

Vulnerabilities
2 medium severity vulnerabilities
Hardening
No application hardening issues

Threats

Tampering
No evidence of software tampering
Malware
4 undesirable applications found

INCIDENTS FOR THIS VERSION:

malware
6 months agoReported By: ReversingLabs (Automated)
Learn more about malware detection
malware
6 months agoReported By: ReversingLabs (Researcher)
See more info on our blog
malware
6 months agoReported By: Community (OpenSSF)
More info

Popularity

2.42k
Total Downloads
1
Contributor
1
Declared Dependencies
0
Dependents

Top issues

See all issues

Problem

Potentially unwanted applications (PUAs) can be considered a risk by some software users. This threat type typically collects private user data, or in more extreme cases, tampers with system security settings. Most threat prevention solutions detect and block PUAs. Software packages that trigger security solution detections also tend to increase the number of support calls and open tickets from users.

Prevalence in PyPI community

1 packages
found in
Top 100
5 packages
found in
Top 1k
18 packages
found in
Top 10k
1306 packages
in community

Next steps

Revise the use of components that raise these alarms. If you can't deprecate those components, make sure they are well-documented.

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 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.

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.

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. Open source communities depend on the work of thousands of software developers that volunteer their time to maintain software components. Software developers build up the reputation of their open source projects by developing in public. Modern source code repositories have many social features that allow software developers to handle bug reports, have discussions with their users, and convey reaching significant project milestones. It is uncommon to find open source projects that omit linking their component to a publicly accessible source code repository.

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

No behavior prevalence information at this time

Prevalence in PyPI community

Behavior often found in this community (Common)
2 packages
found in
Top 100
36 packages
found in
Top 1k
399 packages
found in
Top 10k
30468 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
97 packages
found in
Top 100
816 packages
found in
Top 1k
6970 packages
found in
Top 10k
463877 packages
in community

Prevalence in PyPI community

Behavior often found in this community (Common)
96 packages
found in
Top 100
834 packages
found in
Top 1k
7732 packages
found in
Top 10k
505269 packages
in community

Top vulnerabilities

See all vulnerabilities
Vulnerability Exploitation Lifecycle
(2 Active Vulnerabilities)
None
2 (2 Fixable)
CVE-2025-2953m
CVE-2025-3730m
None
None
Exploits Unknown
Exploits Exist
Exploited by Malware
Patching Mandated

FAQ for ai-labs-snippets-sdk

What is ai-labs-snippets-sdk?

AI Labs Snippets SDK for Python. For more information about this package, visit its homepage.

Is ai-labs-snippets-sdk malicious or is it safe to use?

The Python package ai-labs-snippets-sdk 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 ai-labs-snippets-sdk package were malicious.

Is ai-labs-snippets-sdk popular?

The Python package ai-labs-snippets-sdk is not widely used. It has 2K 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 ai-labs-snippets-sdk once it is in my app?

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