  Attack graphs are a tool for analyzing security vulnerabilities that capture
different and prospective attacks on a system. As a threat modeling tool, it
shows possible paths that an attacker can exploit to achieve a particular goal.
However, due to the large number of vulnerabilities that are published on a
daily basis, they have the potential to rapidly expand in size. Consequently,
this necessitates a significant amount of resources to generate attack graphs.
In addition, generating composited attack models for complex systems such as
self-adaptive or AI is very difficult due to their nature to continuously
change. In this paper, we present a novel fragment-based attack graph
generation approach that utilizes information from publicly available
information security databases. Furthermore, we also propose a domain-specific
language for attack modeling, which we employ in the proposed attack graph
generation approach. Finally, we present a demonstrator example showcasing the
attack generator's capability to replicate a verified attack chain, as
previously confirmed by security experts.
