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@vijaysm The usual handling of shared libraries unfortunately doesn't apply here as hdf5 is pretty aggressive about ensuring headers and libraries are of the same version to rule out errors resulting from data format changes. Here's what it looks like if you don't re-compile a library that depends on hdf5:
The HDF5 header files used to compile this application do not match
the version used by the HDF5 library to which this application is linked.
Data corruption or segmentation faults may occur if the application continues.
This can happen when an application was compiled by one version of HDF5 but
linked with a different version of static or shared HDF5 library.
You should recompile the application or check your shared library related
settings such as 'LD_LIBRARY_PATH'.
'HDF5_DISABLE_VERSION_CHECK' environment variable is set to 1, application will
continue at your own risk.
Headers are 1.8.16, library is 1.8.15
Additionally, conda itself requires an explicit build due to the version pin in recipe/meta.yaml - it won't be able to find a SAT solution that allows the upgraded hdf5 to live alongside a non-upgraded itk.
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A few CVEs were reported and fixed in hdf5 back in November 2016: http://blog.talosintelligence.com/2016/11/hdf5-vulns.html
The hdf5 release notes for that release are available here: https://support.hdfgroup.org/ftp/HDF5/current18/src/hdf5-1.8.18-RELEASE.txt
For additional info, please see: conda-forge/hdf5-feedstock#68 and conda-forge/hdf5-feedstock#71