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

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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.

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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.

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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.

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