Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion

We present a reproducible pipeline for mapping Common Vulnerabilities and Exposures CVEs to MITRE ATT&CK Enterprise techniques from free-text vulnerability descriptions. Rather than relying on the CWE-CAPEC-ATT&CK derivation chain, whose table-expansion artifacts we quantify, we train a multi-lab...

Compute-Budgeted Exploitability Evidence Graphs for Prospective Vulnerability Triage

Defenders cannot patch every newly disclosed vulnerability at once, so exploitability prediction must be evaluated prospectively rather than retrospectively. We study compute-budgeted vulnerability triage in which each CVE is scored only from public evidence visible by a fixed decision time...

A Practical Framework for Evaluating Medical AI Security: Reproducible Assessment of Jailbreaking and Privacy Vulnerabilities across Clinical Specialties

Medical Large Language Models LLMs are increasingly deployed for clinical decision support across diverse specialties, yet systematic evaluation of their robustness to adversarial misuse and privacy leakage remains inaccessible to most researchers. Existing security benchmarks require GPU cluster...

OmniSafeBench-MM: A Unified Benchmark and Toolbox for Multimodal Jailbreak Attack-Defense Evaluation

Recent advances in multi-modal large language models MLLMs have enabled unified perception-reasoning capabilities, yet these systems remain highly vulnerable to jailbreak attacks that bypass safety alignment and induce harmful behaviors. Existing benchmarks such as JailBreakV-28K, MM-SafetyBench,...

Boosting Gradient Leakage Attacks: Data Reconstruction in Realistic FL Settings

Federated learning FL enables collaborative model training among multiple clients without the need to expose raw data. Its ability to safeguard privacy, at the heart of FL, has recently been a hot-button debate topic. To elaborate, several studies have introduced a type of attacks known as gradie...