Measuring the Effect of Induced Persona on Agenda Creation in Language-based Agents for Cyber Deception (2024)

Abstract: This paper presents the SANDMAN architecture for cyber deception, employing Language Agents to create convincing human simulacra. These “Deceptive Agents” serve as advanced cyber decoys, designed to engage attackers to extend the observation period of attack behaviours. This research demonstrates the viability of persona-driven Deceptive Agents to generate plausible human activity to enhance the effectiveness of cyber deception strategies. Through experimentation, measurement and analysis, we illustrate how a prompt schema induces specific “personalities”, defined by the five-factor model of personality, in Large Language Models to generate measurably diverse, and plausible, behaviours.

## introduction autonomous agents are systems embedded within environments, capable of autonomous interaction to influence future conditions, driven by programmed objectives @xcite . historically, agent autonomy was enabled through simple heuristic policies or learned behaviors within defined constraints @xcite @xcite . however, recent advances in the field of generative artificial intelligence (gen-ai) are radically transforming intelligent agent technologies. the most noteworthy and pertinent are large language models (llms) which have demonstrated a remarkable ability to generate human-like text, answer complex questions, and perform other language-driven tasks with high accuracy @xcite . as such, there is growing interest in applying these models as autonomous agent controllers to yield more human-like decision-making capabilities @xcite @xcite . this approach exploits an llm's comprehensive internal model of the world, enhanced by transformer architectures that capture long-range dependencies in text @xcite , to inform actions without domain-specific training. in parallel, researchers have extended llms with memory and planning functions to enhance an agents' humanlike capabilities @xcite @xcite , leading to the concept of language agents @xcite @xcite . novel applications using autonomous agents within security-centric applications include: automating red teaming exercises @xcite , enhancing anomaly detection systems @xcite and, streamlining threat intelligence analysis @xcite . however, to the best of our knowledge, no research has explored their application suited for active cyber defense strategies @xcite , aimed at disrupting early stage cyberadversary activities @xcite . cyber deception research focuses on game-theoretic techniques @xcite and deception technology @xcite to deceive malicious actors via means of mimicry, camouflage, obfuscation etc. this paper introduces the concept of deceptive agents as entities employing generative models to deceive attackers with plausible (mis-)information and behaviours to disrupt attack progress. our work presents an architecture to endow agents with the capability to accumulate, synthesise, and utilise memories facilitating the generation of contextually relevant, plausible behavior that dynamically adjusts to experiences and environments. in summary, this paper makes the following contributions: the remainder of the paper is structured as follows: section 2 outlines related work, section 3 presents the sandman architecture to operate deceptive agents, section 4 outlines experiments and analyses performed concerning the controlled induction of personas within llms based on the five-factor model (ffm), section 5 provides a discussion of the findings, including directions for future work, and section 6 presents the conclusion. ## related work prior research has explored design considerations and behaviours of autonomous agents, the utility and efficacy of llms in security-focused applications, and identifying existing issues within traditional defensive deception strategies. these are key domains of study to realising deceptive agents. llms in defensive applications: gen-ai presents a series of new opportunities for cybersecurity. researchers have explored utilising llms within security-focused applications, demonstrating their potential in automating and streamlining complex security processes. notable advancements include their application to software security testing @xcite , log-based analytics @xcite , unstructured text analysis for threat intelligence @xcite , and security-based training @xcite . language agents: an emerging class of autonomous agent leveraging llms as central controllers to direct actions @xcite @xcite . research has introduced bespoke architectures and frameworks for language agents (las) providing varied applications across diverse environments. these include the simulation of multi-agent sandbox environments to study inter-agent behaviour @xcite , collaborative frameworks in software development @xcite , and the integration of agents within video games @xcite . these studies underscore the proficiency of llms to manage complex, autonomous agent behaviours. however, the existing literature primarily explores these agents in non-security contexts or in scenarios where the environment or application sets inherent limitations on their utility. agent architectures: whilst the concept of las is relatively straightforward (i.e., using a llm as an autonomous agent controller), achieving the intended effect (i.e., long-horizon task completion) is typically far more complex @xcite . this has led to new frameworks to categorise existing agents and plan future developments. the cognitive architecture for language agents (coala), is a comprehensive approach which draws on cognitive science and symbolic ai to characterise general purpose architectures for las @xcite . coala organises agents along three key dimensions: their information storage (memories); action space (internal/external); and decision-making procedures (interactive loop with planning and execution). the core components of the coala framework are provided below: ## deceptive agent architecture in this section we provide the architecture for sandman, a software platform for ai-driven autonomous agents in generating plausible behaviours within a digital environment. at its core, sandman represents a novel contribution to the emerging research field concerning cognitive architectures and language agents @xcite . modular and extensible by design, sandman enables fine-tuning of agents to support various applications, ranging from human-like gray agent simulation for cyber-warfare exercising and defender emulation, to augmenting deception platforms to provide dynamic and plausible environments. the goal of sandman is not to interact with other humans or agents. rather, it is intended to produce plausible simulacra representing humanlike actions in digital environments that, to the observer, cannot be distinguished from human. a particular focus area sandman seeks to address concerns generative deception, a novel concept that, to the best of our knowledge, has not yet been explored in the context of autonomous agents. ## persona-based task planning in llms planning modules are essential for autonomous agents, enabling structured and controlled behavior @xcite . as demonstrated in existing studies @xcite @xcite , planning heavily influences activities performed by agent(s). in sandman, the planning functionality is provided by the bootstrap task. initial debugging and development used a simple rule-based approach to generate an agent's plan, validating that the execution flow aligned with the architecture's design. this approach involved appending tasks sequentially in a straightforward, deterministic manner. however, task scheduling via an llm presents a novel and unexplored opportunity. although llms have been used similarly in other contexts @xcite , there has been no systematic investigation into the relationship between persona generation and the resulting task outputs. typically, the variance in outputs is either asserted or assumed without rigorous analysis. in this section, we demonstrate the structured creation and induction of personas into llms presents distinct effects on associated, llm-generated schedules. ## discussion this study demonstrates the controlled induction of personality traits, based on ffm, can produce distinctly different planning-based behaviours within an llm. this is essential for the deceptive agents herein proposed, operated by the sandman architecture, to be effective in their capacity to create plausibly deniable behaviours and misinformation which cannot be distinguished from human and machine. the aim hereby is to enable defenders the capability to craft and refine various simulacra personas of autonomous agents in security-focused applications. while the central focus is on deploying decoys to gather intelligence on attackers, the concept and research herein raises question toward the efficacy of low-cost, large-scale deployment of deceptive agents to achieve a dazzling effect toward adversaries. here, a large number of agents operate autonomously to simulate entire networks of interconnected systems and individuals, thereby making it difficult for attackers to distinguish between real assets and decoys. lastly, it must be noted that this study is observational in nature. its central aim is to investigate whether induced personas within an llm presents considerable effect upon planning-based behaviour within a downstream task. exploration of any observed correlation or relationship between a given ocean trait and associated output is suited toward future work, outlined below. ## conclusion this paper introduces the concept of deceptive agents-a new class of autonomous agents leveraging llms as its central controller whose purpose is to deceive adversaries by exhibiting plausible, human-like behaviour. agents operate on a novel architecture, inspired by the coala framework, which offers an extensible, modular platform for developing language agents. this study highlights the use of llms in generating context relevant to the operation of the deceptive agent and, importantly, utilises llms for task planning, which is influenced by the induction of one of the big-five (ocean) personality traits, based on ffm. the work introduces a schema for personality prompt generation that produces statistically significant schedule populations in terms of task frequency and duration. the results underscore the utility and effectiveness of using llms in such decisionmaking processes in language agents, employing personality traits as a control mechanism to craft distinct personas.