  The rapid advancement of Artificial Intelligence (AI) has led to its
integration into various areas, especially with Large Language Models (LLMs)
significantly enhancing capabilities in Artificial Intelligence Generated
Content (AIGC). However, the complexity of AI systems has also exposed their
vulnerabilities, necessitating robust methods for failure analysis (FA) and
fault injection (FI) to ensure resilience and reliability. Despite the
importance of these techniques, there lacks a comprehensive review of FA and FI
methodologies in AI systems. This study fills this gap by presenting a detailed
survey of existing FA and FI approaches across six layers of AI systems. We
systematically analyze 160 papers and repositories to answer three research
questions including (1) what are the prevalent failures in AI systems, (2) what
types of faults can current FI tools simulate, (3) what gaps exist between the
simulated faults and real-world failures. Our findings reveal a taxonomy of AI
system failures, assess the capabilities of existing FI tools, and highlight
discrepancies between real-world and simulated failures. Moreover, this survey
contributes to the field by providing a framework for fault diagnosis,
evaluating the state-of-the-art in FI, and identifying areas for improvement in
FI techniques to enhance the resilience of AI systems.
