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Miguel Tulla

Publications and source records attributed to Miguel Tulla.

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Hybrid Privilege Escalation and Remote Code Execution Exploit Chains

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.

cs.CR

LLM-Supported Natural Language to Bash Translation

The Bourne-Again Shell (Bash) command-line interface for Linux systems has complex syntax and requires extensive specialized knowledge. Using the natural language to Bash command (NL2SH) translation capabilities of large language models (LLMs) for command composition circumvents these issues. However, the NL2SH performance of LLMs is difficult to assess due to inaccurate test data and unreliable heuristics for determining the functional equivalence of Bash commands. We present a manually verified test dataset of 600 instruction-command pairs and a training dataset of 40,939 pairs, increasing the size of previous datasets by 441% and 135%, respectively. Further, we present a novel functional equivalence heuristic that combines command execution with LLM evaluation of command outputs. Our heuristic can determine the functional equivalence of two Bash commands with 95% confidence, a 16% increase over previous heuristics. Evaluation of popular LLMs using our test dataset and heuristic demonstrates that parsing, in-context learning, in-weight learning, and constrained decoding can improve NL2SH accuracy by up to 32%. Our findings emphasize the importance of dataset quality, execution-based evaluation and translation method for advancing NL2SH translation. Our code is available at https://github.com/westenfelder/NL2SH

cs.CL