The increasing availability of cloud computing and scientific super computers
brings great potential for making R accessible through public or shared
resources. This allows us to efficiently run code requiring lots of cycles and
memory, or embed R functionality into, e.g., systems and web services. However
some important security concerns need to be addressed before this can be put in
production. The prime use case in the design of R has always been a single
statistician running R on the local machine through the interactive console.
Therefore the execution environment of R is entirely unrestricted, which could
result in malicious behavior or excessive use of hardware resources in a shared
environment. Properly securing an R process turns out to be a complex problem.
We describe various approaches and illustrate potential issues using some of
our personal experiences in hosting public web services. Finally we introduce
the RAppArmor package: a Linux based reference implementation for dynamic
sandboxing in R on the level of the operating system