A closed-loop LLM simulation with genetic algorithms suggests that phishing strategies can evolve to bypass simulated victims' defenses, but the result has not been validated against real humans.
Psychological Profiling in Cybersecurity: A Look at LLMs and Psycholinguistic Features
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abstract
The increasing sophistication of cyber threats necessitates innovative approaches to cybersecurity. In this paper, we explore the potential of psychological profiling techniques, particularly focusing on the utilization of Large Language Models (LLMs) and psycholinguistic features. We investigate the intersection of psychology and cybersecurity, discussing how LLMs can be employed to analyze textual data for identifying psychological traits of threat actors. We explore the incorporation of psycholinguistic features, such as linguistic patterns and emotional cues, into cybersecurity frameworks. Our research underscores the importance of integrating psychological perspectives into cybersecurity practices to bolster defense mechanisms against evolving threats.
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Can We End the Cat-and-Mouse Game? Simulating Self-Evolving Phishing Attacks with LLMs and Genetic Algorithms
A closed-loop LLM simulation with genetic algorithms suggests that phishing strategies can evolve to bypass simulated victims' defenses, but the result has not been validated against real humans.