Inducing Personality in LLM-Based Honeypot Agents: Measuring the Effect on Human-Like Agenda Generation
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910893029195776 |
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| author | Newsham, Lewis Hyland, Ryan Prince, Daniel |
| author_facet | Newsham, Lewis Hyland, Ryan Prince, Daniel |
| contents | This paper presents SANDMAN, an architecture for cyber deception that leverages Language Agents to emulate convincing human simulacra. Our 'Deceptive Agents' serve as advanced cyber decoys, designed for high-fidelity engagement with attackers by extending the observation period of attack behaviours. Through experimentation, measurement, and analysis, we demonstrate how a prompt schema based on the five-factor model of personality systematically induces distinct 'personalities' in Large Language Models. Our results highlight the feasibility of persona-driven Language Agents for generating diverse, realistic behaviours, ultimately improving cyber deception strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19752 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inducing Personality in LLM-Based Honeypot Agents: Measuring the Effect on Human-Like Agenda Generation Newsham, Lewis Hyland, Ryan Prince, Daniel Artificial Intelligence Multiagent Systems This paper presents SANDMAN, an architecture for cyber deception that leverages Language Agents to emulate convincing human simulacra. Our 'Deceptive Agents' serve as advanced cyber decoys, designed for high-fidelity engagement with attackers by extending the observation period of attack behaviours. Through experimentation, measurement, and analysis, we demonstrate how a prompt schema based on the five-factor model of personality systematically induces distinct 'personalities' in Large Language Models. Our results highlight the feasibility of persona-driven Language Agents for generating diverse, realistic behaviours, ultimately improving cyber deception strategies. |
| title | Inducing Personality in LLM-Based Honeypot Agents: Measuring the Effect on Human-Like Agenda Generation |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2503.19752 |