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    failIncident: Malware
    Scanned: about 3 hours ago

    numpy

    2.5.4
    Artifact:
    numpy-2.5.4-cp315-cp315t-win_arm64.whl
    latest
    malicious
    key project
    Fundamental package for array computing in Python
    License: unknown
    Published: 13 days ago
    Visit GitHub
    See on PyPI

    SAFE Assessment

    Compliance

    Licenses
    No license compliance issues
    Secrets
    No sensitive information found

    Security

    Vulnerabilities
    No known vulnerabilities detected
    Hardening
    40 low priority mitigations absent

    Threats

    Tampering
    No evidence of software tampering
    Malware
    1 malicious components found

    INCIDENTS FOR THIS VERSION:

    malware
    about 3 hours agoReported By: ReversingLabs (Automated)
    Learn more about malware detection
    Overviewissues4vulnerabilities0malware1behaviors115dependencies0contributors4versions195

    FAQ for numpy

    What is numpy?

    Fundamental package for array computing in Python.

    Is numpy malicious or is it safe to use?

    The Python package numpy 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. 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. The package numpy had 3 incidents in versions published within 2 years prior to the latest.

    Is numpy popular?

    The Python package numpy is classified as a key project because of its widespread usage and impact. numpy ranks among the top 100 projects in the Python community. It has 24B 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 numpy once it is in my app?

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