2 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
SSP-Bench: A Hybrid Data Generation Framework for Safety, Security, and Privacy Evaluation
Fatih Deniz, Yazan Boshmaf, Issa Khalil
Evaluation of large language models (LLMs) for safety, security, and privacy (SSP) relies heavily on static benchmarks, which suffer from score saturation, data contamination, and…
aiXamine: Unified Black-Box Evaluation of Cross-Dimensional Trade-offs in LLM Safety, Security, and Privacy
Fatih Deniz, Yazan Boshmaf, Dorde Popovic +1
The critical failure modes in deployed large language models (LLMs) are cross-dimensional: a model can score 99.3 in safety alignment while refusing one in three benign queries, or…
aiXamine: Simplified LLM Safety and Security
Fatih Deniz, Dorde Popovic, Yazan Boshmaf +4
Evaluating Large Language Models (LLMs) for safety and security remains a complex task, often requiring users to navigate a fragmented landscape of ad hoc benchmarks, datasets, met…
DeBackdoor: A Deductive Framework for Detecting Backdoor Attacks on Deep Models with Limited Data
Dorde Popovic, Amin Sadeghi, Ting Yu +2
Backdoor attacks are among the most effective, practical, and stealthy attacks in deep learning. In this paper, we consider a practical scenario where a developer obtains a deep mo…
MANTIS: Detection of Zero-Day Malicious Domains Leveraging Low Reputed Hosting Infrastructure
Fatih Deniz, Mohamed Nabeel, Ting Yu +1
Internet miscreants increasingly utilize short-lived disposable domains to launch various attacks. Existing detection mechanisms are either too late to catch such malicious domains…