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Large Language Models (LLMs) have exploded a new heatwave of AI for theirability to engage end-users in human-level conversations with detailed andarticulate answers across many knowledge domains. In response to their fastadoption in many industrial applications, this survey concerns their safety andtrustworthiness. First, we review known vulnerabilities and limitations of theLLMs, categorising them into inherent issues, attacks, and unintended bugs.Then, we consider if and how the Verification and Validation (V&V) techniques,which have been widely developed for traditional software and deep learningmodels such as convolutional neural networks as independent processes to checkthe alignment of their implementations against the specifications, can beintegrated and further extended throughout the lifecycle of the LLMs to providerigorous analysis to the safety and trustworthiness of LLMs and theirapplications. Specifically, we consider four complementary techniques:falsification and evaluation, verification, runtime monitoring, and regulationsand ethical use. In total, 370+ references are considered to support the quickunderstanding of the safety and trustworthiness issues from the perspective ofV&V. While intensive research has been conducted to identify the safety andtrustworthiness issues, rigorous yet practical methods are called for to ensurethe alignment of LLMs with safety and trustworthiness requirements.

A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation