OpenAttack: An Open-source Textual Adversarial Attack Toolkit

NLPGym -- A toolkit for evaluating RL agents on Natural Language Processing Tasks

Reinforcement learning (RL) has recently shown impressive performance in complex game AI and robotics tasks. To a large extent, this is thanks to the availability of simulated environments such as OpenAI Gym, Atari Learning Environment, or Malmo which allow agents to learn complex tasks through interaction with virtual environments. While RL is also increasingly applied to natural language processing (NLP), there are no simulated textual environments available for researchers to apply and consistently benchmark RL on NLP tasks. With the work reported here, we therefore release NLPGym, an open-…

Identifying Adversarial Attacks on Text Classifiers

The landscape of adversarial attacks against text classifiers continues to grow, with new attacks developed every year and many of them available in standard toolkits, such as TextAttack and OpenAttack. In response, there is a growing body of work on robust learning, which reduces vulnerability to these attacks, though sometimes at a high cost in compute time or accuracy. In this paper, we take an alternate approach -- we attempt to understand the attacker by analyzing adversarial text to determine which methods were used to create it. Our first contribution is an extensive dataset for attack …

A Unified Evaluation of Textual Backdoor Learning: Frameworks and Benchmarks

Textual backdoor attacks are a kind of practical threat to NLP systems. By injecting a backdoor in the training phase, the adversary could control model predictions via predefined triggers. As various attack and defense models have been proposed, it is of great significance to perform rigorous evaluations. However, we highlight two issues in previous backdoor learning evaluations: (1) The differences between real-world scenarios (e.g. releasing poisoned datasets or models) are neglected, and we argue that each scenario has its own constraints and concerns, thus requires specific evaluation pro…

Audio Jailbreak：面向大型音频语言模型越狱攻击的开放综合基准

Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Models

该研究提出首个评估大型音频语言模型越狱漏洞的基准AJailBench，构建了含1495条对抗音频的基础数据集与经贝叶斯优化的扩展扰动数据集，实验发现微小语义保留扰动即可大幅降低主流模型安全性，凸显了研发鲁棒语义感知防御机制的必要性。

MS-Exam-Gen：用于评估LLM在多发性硬化MRI文本知识上的源接地基准构建

MS-Exam-Gen: An Automatic Benchmark Construction Toolkit for Evaluating LLMs on Multiple Sclerosis-MRI Imaging Knowledge

MS-Exam-Gen提出一个可复现框架，从66个来源构建含3,058个多选题的MS-MRI知识基准，通过自动审计评估12个LLM，准确率范围46.9%-89.7%，支持项目级和主题级评估。

从对抗军备竞赛到以模型为中心的评估：推动统一的自动鲁棒性评估框架

From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework

本文提出统一的自动鲁棒性评估框架，通过确定评估维度、建立现实评估协议并以扰动程度控制样本有效性，实现从算法为中心到模型为中心的评估转变，并开发 RobTest 工具包验证其有效性。