# Evaluating Open-Weight LLMs for Generating Structured Threat Information for Autonomous Vehicle Vulnerabilities

arXiv: https://arxiv.org/abs/2607.16175 · HTML: https://mlsanity.com/paper/2607.16175/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Builds on (most cited first, 6 of 6)

- [Time for aCTIon: Automated Analysis of Cyber Threat Intelligence in the Wild](https://mlsanity.com/paper/2307.10214/) (2023)
- [What are the attackers doing now? Automating cyber threat intelligence extraction from text on pace with the changing threat landscape: A survey](https://mlsanity.com/paper/2109.06808/) (2021)
- [STIXnet: A Novel and Modular Solution for Extracting All STIX Objects in CTI Reports](https://mlsanity.com/paper/2303.09999/) (2023)
- [ThreatPilot: Attack-Driven Threat Intelligence Extraction](https://mlsanity.com/paper/2412.10872/) (2024)
- [Security Vulnerabilities in Software Supply Chain for Autonomous Vehicles](https://mlsanity.com/paper/2509.16899/) (2025)
- [What Are Adversaries Doing? Automating Tactics, Techniques, and Procedures Extraction: A Systematic Review](https://mlsanity.com/paper/2604.02377/) (2026)

## Abstract (arXiv)

Connected and Autonomous Vehicles (CAVs) rely on interconnected software and hardware components, including sensors, Electronic Control Units, in-vehicle infotainment systems, and telematics units, where vulnerabilities can compromise assets, users, and vehicle operations. These vulnerabilities are commonly documented as plain text in the Common Vulnerabilities and Exposures (CVE) database; however, security practitioners require structured information about affected assets, types of weaknesses, and attack behaviors to effectively mitigate the risks from these vulnerabilities. To this end, we evaluate open-weight Large Language Models (LLMs) for generating Structured Threat Information Expression (STIX), a well-known structured format for representing threat information, for CAV-related CVEs. We construct a dataset called CAV-STIXGen that maps CAV vulnerability descriptions to STIX domain objects (SDO), STIX relationship objects (SRO), Common Weakness Enumeration (CWE), and MITRE ATT&CK techniques mappings. Using this dataset, we evaluated 11 open-weight LLMs (4B to 120B parameters) across various prompting strategies and temperatures. Single-model configurations achieve F1 scores of 0.94 for SDO, 0.63 for SRO, and 0.99 for CWE mapping, while complete MITRE ATT&CK mapping remains challenging. In a multi-agent setup, Gemma-4-31B and Codestral-22B achieve F1 scores of 0.91 for SDOs and 0.43 for SROs, respectively. Lastly, we analyze CWE and MITRE ATT&CK co-occurrences to identify recurring threat patterns in the CAV domain, demonstrating how AI-assisted vulnerability-to-STIX translation can automate threat intelligence and prioritize defense in transportation security.