TechniqueRAG: Retrieval Augmented Generation for Adversarial Technique Annotation in Cyber Threat Intelligence Text
Accurately identifying adversarial techniques in security texts is critical for effective cyber defense. However, existing methods face a fundamental trade-off: they either rely on generic models with limited domain precision or require resource-intensive pipelines that depend on large labeled datasets and task-specific optimizations—such as custom hard-negative mining and denoising—resources rarely available in specialized domains. We […]
AZERG STIX Entity and Relationship Extractor
AZERG is a framework for automatically extracting Structured Threat Information Expression (STIX) entities and relationships from unstructured cyber threat intelligence reports. This tool uses fine-tuned language models to assist security analysts in generating STIX-compatible data, streamlining the threat intelligence lifecycle. Source code: https://github.com/qcri/azerg
LLMxCPG: Context-Aware Vulnerability Detection Through Code Property Graph-Guided Large Language Models
Software vulnerabilities present a persistent security challenge, with over 25,000 new vulnerabilities reported in the Common Vulnerabilities and Exposures (CVE) database in 2024 alone. While deep learning based approaches show promise for vulnerability detection, recent studies reveal critical limitations in terms of accuracy and robustness: accuracy drops by up to 45% on rigorously verified datasets, […]