AI КАК ИСТОЧНИК УГРОЗ (LLM, ГЕНЕРАТИВНЫЙ ИИ). РИСКИ ИСПОЛЬЗОВАНИЯ ГЕНЕРАТИВНЫХ ЯЗЫКОВЫХ МОДЕЛЕЙ В ФИШИНГОВЫХ АТАКАХ: АНАЛИЗ И МОДЕЛЬ УГРОЗ
Comparative analysis of existing academic approaches to the study of AI in phishing
compiled by the author based on [5], [6], [7], [8]
Direction | Focus of the study | Purpose | Relevance for threat modeling |
Human factor | Recipient behavior and vulnerabilities | Explains the causes of attack success | Enables the identification of victim vulnerabilities |
AI-enhanced social engineering | AI as a tool of persuasive influence | Demonstrates increased personalization and plausibility | Links LLMs with influence techniques |
Phishing content generation | Creation of phishing messages using LLMs | Reveals offensive capabilities of AI | Describes the preparation and delivery stages |
Detection of AI-enabled phishing | Methods for identifying AI-generated attacks | Forms the basis for new defensive solutions | Establishes foundations for countermeasure |
Protection of LLM-based systems | Threats to LLM infrastructure and protection measures | Expands risk understanding beyond the message itself | Incorporates protection of the LLM environment into the threat model |
Multi-agent detection systems | Architectures for intelligent threat detection | Enhances prospects for threat detection | Refines future directions of defensive strategies |