6 papers
Large Language Models Still Exhibit Bias in Long Text
Wonje Jeung, Dongjae Jeon, Ashkan Yousefpour +1
Existing fairness benchmarks for large language models (LLMs) primarily focus on simple tasks, such as multiple-choice questions, overlooking biases that may arise in more complex…
M2S: Multi-turn to Single-turn jailbreak in Red Teaming for LLMs
Junwoo Ha, Hyunjun Kim, Sangyoon Yu +4
We introduce a novel framework for consolidating multi-turn adversarial ``jailbreak'' prompts into single-turn queries, significantly reducing the manual overhead required for adve…
ELITE: Enhanced Language-Image Toxicity Evaluation for Safety
Wonjun Lee, Doehyeon Lee, Eugene Choi +5
Current Vision Language Models (VLMs) remain vulnerable to malicious prompts that induce harmful outputs. Existing safety benchmarks for VLMs primarily rely on automated evaluation…
Representation Bending for Large Language Model Safety
Ashkan Yousefpour, Taeheon Kim, Ryan S. Kwon +7
Large Language Models (LLMs) have emerged as powerful tools, but their inherent safety risks - ranging from harmful content generation to broader societal harms - pose significant…
sudo rm -rf agentic_security
Sejin Lee, Jian Kim, Haon Park +3
Large Language Models (LLMs) are increasingly deployed as computer-use agents, autonomously performing tasks within real desktop or web environments. While this evolution greatly e…
Selective Vision is the Challenge for Visual Reasoning: A Benchmark for Visual Argument Understanding
Jiwan Chung, Sungjae Lee, Minseo Kim +4
Visual arguments, often used in advertising or social causes, rely on images to persuade viewers to do or believe something. Understanding these arguments requires selective vision…