Md Rayhanur Rahman

Ahmed Ryan, Saad Sakib Noor, Md Erfan et al.

Classifying Cyber Threat Intelligence (CTI) using MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) is essential for proactive defense, but historically required extensive human effort. Pre-Large Language Model (LLM) automation sped up this process, but could not resolve the complex language and multi-step attack patterns found in unstructured CTI reports. LLMs addressed previous limitations by using contextual reasoning to understand unstructured text. However, current evaluations rely on simplified, single-technique sentences that ignore the complexity of real-world CTI reports, which often leads to inflated performance results. Consequently, the baseline performance of open-source LLMs on complex unstructured CTI reports remains unevaluated. To address this gap, we constructed a ground-truth dataset of 2,076 human-annotated sentences (1,281 technique-positive, 795 negative) from 83 complex unstructured CTI reports. These sentences were mapped to 114 unique ATT&CK techniques using a six-phase annotation process, achieving \k{appa} = 0.68 inter-annotator agreement. Using this dataset, we evaluated seven open-source LLMs ranging from 8B to 236B parameters across prompt strategy and temperature configurations. The highest-performing LLM achieved a micro-averaged F1 score of 0.22, establishing the empirical baseline for multi-label ATT&CK classification on complex unstructured CTI. Parameter size showed a statistically significant positive correlation with F1 score. Prompt strategy and temperature produced no statistically significant gains across model configurations. These results indicate that current open-source LLMs are insufficient for production-grade ATT&CK classification. The dataset, benchmark, and findings provide a reproducible foundation for future CTI research.

Md Nazmul Haque, Sivana Hamer, Brandon Wroblewski et al.

Large-scale cyberattacks, referred to as campaigns, are documented across multiple CTI reports from diverse sources, with some providing a high-level overview of attack techniques and others providing technical details. Extracting attack techniques from reports is essential for organizations to identify the controls required to protect against attacks. Manually extracting techniques at scale is impractical. Existing automated methods focus on single reports, leaving many attack techniques and their controls undetected, resulting in a fragmented view of campaign behavior. The goal of this study is to aid security researchers in extracting attack techniques and controls from a campaign by replicating and comparing the performance of the state-of-the-art ATT&CK technique extraction methods in a multi-report campaign setting compared to prior single-report evaluations. We conduct an empirical study of 29 methods to extract attack techniques, spanning named entity recognition (NER), encoder-based classification, and decoder-based LLM approaches. Our study analyzes 90 CTI reports across three major attack campaigns: SolarWinds, XZ Utils, and Log4j, using both quantitative performance metrics and their impact on controls. Our results show that aggregating multiple CTI reports improves the F1 score by about 26% over single-report analysis, with most approaches reaching performance saturation after 5--15 reports. Despite these gains, extraction performance remains limited, with maximum F1 scores of 78.6% for SolarWinds and 54.9% for XZ Utils. Moreover, up to 33.3% of misclassifications involve semantically similar techniques that share tactics and overlap in descriptions. The misclassification has a disproportionate effect on control coverage. Reports that are longer and include technical details consistently perform better, even though their readability scores are low.