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Moayad Aloqaily

ACL 2026×2ICML 2026×2安全可解释性强化学习与后训练推理数据与预训练
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0 一作 · 4 篇 · ACL 2026 / ICML 2026

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2026 顶会论文

4
作者位次 #3/8ACL 2026Poster· Natural Language Processing

Backdoor Collapse: Eliminating Unknown Threats Via Known Backdoor Aggregation In Language Models

Liang Lin · Miao Yu · Moayad Aloqaily · Zhenhong Zhou · Kun Wang · Linsey Pang · Prakhar Mehrotra · Qingsong Wen

摘要

Backdoor attacks are a significant threat to large language models (LLMs), often embedded via public checkpoints, yet existing defenses rely on impractical assumptions about trigger settings. To address this challenge, we propose Locphylax, a defense framework that requires no prior knowledge of...

安全可解释性强化学习与后训练OpenReview 项目页
作者位次 #6/11ACL 2026Findings· Findings of ACL

H ear S ay Benchmark: Do Audio LLM s Leak What They Hear?

Jin Wang · Kaiwen Luo · Liang Lin · Weiliu Wang · Yitian Chen · Moayad Aloqaily · Xuehai Tang · Zhenhong Zhou · Kun Wang · Li Sun · Qingsong Wen

摘要

While Audio Large Language Models (ALLMs) have achieved remarkable progress in understanding and generation, their potential privacy implications remain largely unexplored. This paper takes the first step to investigate whether ALLMs inadvertently leak user privacy solely through acoustic voicepr...

推理安全数据与预训练OpenReview 项目页

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