Large Language Models (LLMs) and Deep-Learning Frameworks as Independent Threat Actors

This chapter examines large language models (LLMs) and deep-learning frameworks as emerging independent threat actors, surveying empirical evidence that autonomous agents can identify vulnerabilities, generate exploits, escalate privileges, and adapt tactics with minimal human direction. It reviews adversarial machine learning foundations, documented autonomous exploitation capabilities, and generative fraud techniques, then examines direct implications for cyber insurance underwriting, including frequency and severity repricing, aggregation risk, underwriting-questionnaire gaps, and policy-wording considerations. The chapter argues that autonomous LLM agents constitute a distinct peril category requiring explicit actuarial treatment rather than incremental extension of existing automated-malware risk models, and outlines practical recommendations for insurers navigating this rapidly evolving threat landscape.

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