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

risky-drinking

書目名稱Large Language Models in Cybersecurity影響因子(影響力)




書目名稱Large Language Models in Cybersecurity影響因子(影響力)學科排名




書目名稱Large Language Models in Cybersecurity網(wǎng)絡(luò)公開度




書目名稱Large Language Models in Cybersecurity網(wǎng)絡(luò)公開度學科排名




書目名稱Large Language Models in Cybersecurity被引頻次




書目名稱Large Language Models in Cybersecurity被引頻次學科排名




書目名稱Large Language Models in Cybersecurity年度引用




書目名稱Large Language Models in Cybersecurity年度引用學科排名




書目名稱Large Language Models in Cybersecurity讀者反饋




書目名稱Large Language Models in Cybersecurity讀者反饋學科排名

易彎曲

Conversational Agents evaluation of model output, which is then used for further fine-tuning. Fine-tuning with . (RLHF) models perform better but are resource-intensive and specific for each model. Another critical difference in the performance of various CA is their ability to access auxiliary services for task delegation.

自然環(huán)境

貨物

牲畜欄

LLM Controls Execution Flow Hijackingtical systems, developing prompt and resulting API calls verification tools, implementing security by designing good practices, and enhancing incident logging and alerting mechanisms can be considered to reduce the novel attack surface presented by LLMs.

無關(guān)緊要

Filibuster

habile

Repatriate

Private Information Leakage in LLMs generative AI. This chapter relates the threat of information leakage with other adversarial threats, provides an overview of the current state of research on the mechanisms involved in memorization in LLMs, and discusses adversarial attacks aiming to extract memorized information from LLMs.

橫截,橫斷