Tensors and Dynamic neural networks in Python with strong GPU acceleration.
The Python package torch 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.
The Python package torch is classified as a key project because of its widespread usage and impact. torch ranks among the top 1000 projects in the Python community. It has 2B 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.
Since we can't know when or where malicious attacks will happen, we recommend tracking how torch behaviors change over multiple software releases. By adopting differential analysis in your release process, you can detect unexpected changes to a torch version, which can prevent advanced software supply chain attacks. Read how our technology can help prevent future attacks similar to SolarWinds and 3CX.
... 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.