# Personality Emulation Utilizing Large Language Models
## 1 Introduction  
The use of fake identities has proven to be an effective method for performing active open-source intelligence (OSINT) and privacy research [1, 2]. The Use & Abuse project at the Virginia Tech National Security Institute has created and developed the architecture necessary to generate and deploy fake identities at scale [2, 3]. Previous research has identified that account activity and communications are key indicators used to detect fake accounts on the Internet [4, 5, 6]. Current fake identity models, however, are largely stagnant after deployment to target websites. Therefore, to conduct privacy research with fake identities in more complex Internet domains and establish persistent and human-passable digital identities, it is necessary to develop a method to provide fake identities with the capability of actively responding and interacting with received content. Additionally, to enhance credibility, these responses must be cohesive, realistic, and demonstrate consistent personality traits. The work performed in this paper investigates the potential application of LLMs to generate personality-reflective email responses for fake identity applications. Previous U&A research has identified that activity and responses are key components of establishing an effective fake digital identity for privacy research [2]. The majority of communications received by fake identities are via email; however, identities currently do not generate or send any responses. This lack of capability for identities greatly limits their long-term application as well as their ability to successfully avoid detection in more scrutinized Internet domains. Large language models (LLMs) are one tool that has demonstrated the ability to efficiently generate written content [7, 8, 9] and email responses [10, 11]. Leveraging LLMs to efficiently generate responses at scale for emails received by fake identities represents a potential solution to allow identities to automatically interact. To convincinglypass as human, the emails generated for each identity must maintain a cohesive personality and display consistent behavior across all chains and interactions. Representing personality and generating personality-consistent responses for fake identities is an abstract challenge, particularly since personality is a complex and subjective concept [12]. Therefore, the application of a scientific or psychological model commonly used to represent real human personality can offer a structured and reputable framework. There are numerous psychometric models; however, the most popular and widely accepted are the Five Factor Model (Big Five model) [13] the Myers-Briggs Type Indicator model [14], and the VIA Character Strength model [15]. Researchers have investigated the application and tailoring of personality for various automated communication platforms, including chatbots and large language models [12; 16; 17]. The exploration of using AI and LLMs to mimic personality traits in written content, however, is limited. The work in this paper explores the application of the Big Five model to construct a model for fake identities' personality, as it is the most widely researched model [18]. These profiles are then used as input into an LLM to investigate the application of LLMs to generate emails emulating these personality traits. This research contributes to the long-term goal of the Use & Abuse project to provide fake identities with the ability to respond automatically to received content, enhancing their realism and allowing them to withstand increased scrutiny. Ethical Implications of Fake Identities and LLMs Using LLMs to mimic human beings and, more broadly, for active OSINT applications raises ethical concerns about potential misuse for harmful purposes such as deception and manipulation.