Use of LLMs for Illicit Purposes: Threats, Prevention Measures, and
  Vulnerabilities
Maximilian Mozes Xuanli He Bennett Kleinberg Lewis D. Griffin
Spurred by the recent rapid increase in the development and distribution of
large language models (LLMs) across industry and academia, much recent work has
drawn attention to safety- and security-related threats and vulnerabilities of
LLMs, including in the context of potentially criminal activities.
Specifically, it has been shown that LLMs can be misused for fraud,
impersonation, and the generation of malware; while other authors have
considered the more general problem of AI alignment. It is important that
developers and practitioners alike are aware of security-related problems with
such models. In this paper, we provide an overview of existing - predominantly
scientific - efforts on identifying and mitigating threats and vulnerabilities
arising from LLMs. We present a taxonomy describing the relationship between
threats caused by the generative capabilities of LLMs, prevention measures
intended to address such threats, and vulnerabilities arising from imperfect
prevention measures. With our work, we hope to raise awareness of the
limitations of LLMs in light of such security concerns, among both experienced
developers and novel users of such technologies.