Ensembling LLMs for AI-Augmented Cybersecurity Software Requirements Generation

Translating high-level controls from security standards into concrete, system-specific requirements is central to cybersecurity requirements engineering. Large language models LLMs can accelerate this labor-intensive, recall-sensitive task, but any single run is unreliable: it misses valid...

On Membership Inference Attacks in Knowledge Distillation

Nowadays, Large Language Models LLMs are trained on huge datasets, some including sensitive information. This poses a serious privacy concern because privacy attacks such as Membership Inference Attacks MIAs may detect this sensitive information. While knowledge distillation compresses LLMs into...

R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning

Vision-language models VLMs, such as CLIP, have gained significant popularity as foundation models, with numerous fine-tuning methods developed to enhance performance on downstream tasks. However, due to their inherent vulnerability and the common practice of selecting from a limited set of...