📝 SummarXiv

Abstract

Research on exploit chains predominantly focuses on sequences with one type of exploit, e.g., either escalating privileges on a machine or executing remote code. In networks, hybrid exploit chains are critical because of their linkable vulnerabilities. Moreover, developing hybrid exploit chains is challenging because it requires understanding the diverse and independent dependencies and outcomes. We present hybrid chains encompassing privilege escalation (PE) and remote code execution (RCE) exploits. These chains are executable and can span large networks, where numerous potential exploit combinations arise from the large array of network assets, their hardware, software, configurations, and vulnerabilities. The chains are generated by ALFA-Chains, an AI-supported framework for the automated discovery of multi-step PE and RCE exploit chains in networks across arbitrary environments and segmented networks. Through an LLM-based classification, ALFA-Chains describes exploits in Planning Domain Description Language (PDDL). PDDL exploit and network descriptions then use off-the-shelf AI planners to find multiple exploit chains. ALFA-Chains finds 12 unknown chains on an example with a known three-step chain. A red-team exercise validates the executability with Metasploit. ALFA-Chains is efficient, finding an exploit chain in 0.01 seconds in an enterprise network with 83 vulnerabilities, 20 hosts, and 6 subnets. In addition, it is scalable, it finds an exploit chain in an industrial network with 114 vulnerabilities, 200 hosts, and 6 subnets in 3.16 seconds. It is comprehensive, finding 13 exploit chains in 26.26 seconds in the network. Finally, ALFA-Chains demonstrates flexibility across different exploit sources, ability to generalize across diverse network types, and robustness in discovering chains under constrained privilege assumptions.