skill-safety-bench

SkillSafetyBench SkillSafetyBench 是一个已经物化好的 benchmark，用来评估针对 skill-facing context 的攻击导致的 agent 安全失败。每个 case 都保持用户表层任务是普通任务，同时把攻击放在本地 skills、helper scripts、sidecar files、corpora、templates 或其他 agent 执行时可能读取的 workspace artifacts 中。 欢迎访问 SkillSafetyBench website 查看 benchmark 设计、case...

ATLAS: AI-Assisted Threat-To-Assertion Learning for System-On-Chip Security Verification

This work presents ATLAS, an LLM-driven framework that bridges standardized threat modeling and property-based formal verification for System-on-Chip SoC security. Starting from vulnerability knowledge bases such as Common Weakness Enumeration CWE, ATLAS identifies SoC-specific assets, maps...

LLM-Based Multi-Class Attack Analysis and Mitigation Framework in IoT/IIoT Networks

The Internet of Things has expanded rapidly, transforming communication and operations across industries but also increasing the attack surface and security breaches. Artificial Intelligence plays a key role in securing IoT, enabling attack detection, attack behavior analysis, and mitigation...

Adversarial Attacks on VQA-NLE: Exposing and Alleviating Inconsistencies in Visual Question Answering Explanations

Natural language explanations in visual question answering VQA-NLE aim to make black-box models more transparent by elucidating their decision-making processes. However, we find that existing VQA-NLE systems can produce inconsistent explanations and reach conclusions without genuinely understandi...

Private Federated Learning Using Preference-Optimized Synthetic Data

In practical settings, differentially private Federated learning DP-FL is the dominant method for training models from private, on-device client data. Recent work has suggested that DP-FL may be enhanced or outperformed by methods that use DP synthetic data Wu et al., 2024; Hou et al., 2024. The...