  This paper explores how the current paradigm of vulnerability management
might adapt to include machine learning systems through a thought experiment:
what if flaws in machine learning (ML) were assigned Common Vulnerabilities and
Exposures (CVE) identifiers (CVE-IDs)? We consider both ML algorithms and model
objects. The hypothetical scenario is structured around exploring the changes
to the six areas of vulnerability management: discovery, report intake,
analysis, coordination, disclosure, and response. While algorithm flaws are
well-known in the academic research community, there is no apparent clear line
of communication between this research community and the operational
communities that deploy and manage systems that use ML. The thought experiments
identify some ways in which CVE-IDs may establish some useful lines of
communication between these two communities. In particular, it would start to
introduce the research community to operational security concepts, which
appears to be a gap left by existing efforts.
