Bounty Hunter: Autonomous, Comprehensive Emulation of Multi-Faceted Adversaries
Louis Hackl\"ander-Jansen, Rafael Uetz, Martin Henze

TL;DR
Bounty Hunter is an open-source tool that autonomously emulates complex adversaries in simulated networks, covering diverse tactics and behaviors to improve cybersecurity assessments and research efficiency.
Contribution
It introduces an autonomous adversary emulation method integrated with Caldera, enabling comprehensive, multi-faceted attack simulations without prior target knowledge.
Findings
Achieves autonomous compromise of simulated networks
Provides diverse attack tactics and behaviors
Enhances realism and coverage in adversary emulation
Abstract
Adversary emulation is an essential procedure for cybersecurity assessments such as evaluating an organization's security posture or facilitating structured training and research in dedicated environments. To allow for systematic and time-efficient assessments, several approaches from academia and industry have worked towards the automation of adversarial actions. However, they exhibit significant limitations regarding autonomy, tactics coverage, and real-world applicability. Consequently, adversary emulation remains a predominantly manual task requiring substantial human effort and security expertise - even amidst the rise of Large Language Models. In this paper, we present Bounty Hunter, an automated adversary emulation method, designed and implemented as an open-source plugin for the popular adversary emulation platform Caldera, that enables autonomous emulation of adversaries with…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Information and Cyber Security · Adversarial Robustness in Machine Learning
