TechniqueRAG: Retrieval Augmented Generation for Adversarial Technique
  Annotation in Cyber Threat Intelligence Text
Authors: name: Ahmed Lekssays, name: Utsav Shukla, name: Husrev Taha Sencar, name: Md Rizwan Parvez
Organization: Qatar Computing Research Institute

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 propose TechniqueRAG, a domain-specific retrieval-augmented generation
(RAG) framework that bridges this gap by integrating off-the-shelf retrievers,
instruction-tuned LLMs, and minimal text-technique pairs. Our approach
addresses data scarcity by fine-tuning only the generation component on limited
in-domain examples, circumventing the need for resource-intensive retrieval
training. While conventional RAG mitigates hallucination by coupling retrieval
and generation, its reliance on generic retrievers often introduces noisy
candidates, limiting domain-specific precision. To address this, we enhance
retrieval quality and domain specificity through zero-shot LLM re-ranking,
which explicitly aligns retrieved candidates with adversarial techniques.
  Experiments on multiple security benchmarks demonstrate that TechniqueRAG
achieves state-of-the-art performance without extensive task-specific
optimizations or labeled data, while comprehensive analysis provides further
insights.