Titlebook: Large Language Models in Cybersecurity; Threats, Exposure an Andrei Kucharavy,Octave Plancherel,Vincent Lenders Book‘‘‘‘‘‘‘‘ 2024 The Edito

Infuriate

Adapting LLMs to Downstream Applicationsds is provided, specifically the prompt optimization, pre-prompting and implicit prompting (system prompting), model coordination through actor agents, integration with auxiliary tools, parameter-efficient fine-tuning, further model pre-training, from-scratch retraining, and finally domain-specific distillation.

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Phishing and Social Engineering in the Age of LLMse a comprehensive look, examining how AI technology orchestrates a phishing attack posing as a typical e-commerce transaction and how an LLM was used in a romance-themed cryptocurrency scam. Both scenarios underline the need for increased awareness and improved defenses against these novel and sophisticated cyber threats.

GLUE

矛盾

Tasks for LLMs and Their Evaluationng, Reasoning, and Text Generation. While LLMs have shown promising results, in particular as general models, their capabilities vary depending on their architecture, training dataset, and the nature of the task. We will briefly define the natural language tasks and give an overview of LLMs’ current state of the art.

Spinous-Process

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CAGE

Blood-Vessels

GRIPE

Overview of Existing LLM FamiliesWhile the general public discovered . (LLMs) with ChatGPT—a generative autoregressive model, they are far from the only models in the LLM family. Various architectures and training regiments optimized for specific usages were designed throughout their development, which were then classified as different LLM families.

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