Safe Intelligence for All: Building Adversarially Resilient LLM Interfaces for CTI
TL;DR
A novel Multi-Layer Adversarial Defense Framework (MLADF), a composite metric for evaluating the robustness of CTI LLM interfaces, and a Confidence-Aware Response Sanitization (CARS) technique combining Monte Carlo dropout uncertainty quantification with knowledgebase factgrounding are introduced.
Abstract
The democratization of Cyber Threat Intelligence (CTI) through Large Language Models (LLMs) represents a paradigm shift in organizational security posture, enabling non-expert users to access and interpret complex threat data through intuitive natural language interfaces. However, this democratization introduces a critical paradox: the very mechanisms that lower the barrier to CTI consumption simultaneously expand the attack surface for adversarial manipulation. This paper presents a comprehensive investigation into the dual challenge of CTI democratization and adversarial resilience, proposing a novel Multi-Layer Adversarial Defense Framework (MLADF) and the CTI-SHIELD (Systematic Hardening and Intelligence-Enhanced LLM Defense) algorithm. Our threat model—formalized through the STRIDE methodology—identifies four primary adversarial attack classes: prompt injection, data poisoning, output manipulation, and model extraction, and systematically analyses their exploitation pathways in CTI deployment contexts. We introduce the Adversarial Resistance Score (ARS), a composite metric for evaluating the robustness of CTI LLM interfaces, and a Confidence-Aware Response Sanitization (CARS) technique combining Monte Carlo dropout uncertainty quantification with knowledgebase factgrounding. Experimental evaluation through structured red-team exercises demonstrates that MLADF reduces adversarial attack success rates by an average of 87.3% compared to unprotected baselines while maintaining 91.4% user comprehension accuracy and achieving calibration error (ECE) of 0.028—superior to all competing methods. A human-in-the-loop user study (n=48) confirms that our protected interface reduces adversarial susceptibility by 76.0% (p<0.001) without degrading usability. Ablation studies confirm all four MLADF layers are individually necessary, and cross-domain generalization experiments across five security verticals confirm AUC > 0.90 outside the CTI training domain.