  The exposure of security vulnerabilities in safety-aligned language models,
e.g., susceptibility to adversarial attacks, has shed light on the intricate
interplay between AI safety and AI security. Although the two disciplines now
come together under the overarching goal of AI risk management, they have
historically evolved separately, giving rise to differing perspectives.
Therefore, in this paper, we advocate that stakeholders in AI risk management
should be aware of the nuances, synergies, and interplay between safety and
security, and unambiguously take into account the perspectives of both
disciplines in order to devise mostly effective and holistic risk mitigation
approaches. Unfortunately, this vision is often obfuscated, as the definitions
of the basic concepts of "safety" and "security" themselves are often
inconsistent and lack consensus across communities. With AI risk management
being increasingly cross-disciplinary, this issue is particularly salient. In
light of this conceptual challenge, we introduce a unified reference framework
to clarify the differences and interplay between AI safety and AI security,
aiming to facilitate a shared understanding and effective collaboration across
communities.
