2,077+ academic papers on AI security, attacks, and defenses
Showing 181–200 of 809 papers
Clear filtersYanzhang Fu, Zizheng Guo, Jizhou Luo
Score-based query attacks pose a serious threat to deep learning models by crafting adversarial examples (AEs) using only black-box access to model...
Scott Thornton
Hybrid Retrieval-Augmented Generation (RAG) pipelines combine vector similarity search with knowledge graph expansion for multi-hop reasoning. We...
Benjamin Livshits
We argue that when it comes to producing secure code with AI, the prevailing "fighting fire with fire" approach -- using probabilistic AI-based...
Sahar Zargarzadeh, Mohammad Islam
The Internet of Things (IoT) has revolutionized connectivity by linking billions of devices worldwide. However, this rapid expansion has also...
Pengyu Chang, Yixiong Fang, Silin Chen +3 more
Software testing is a critical, yet resource-intensive phase of the software development lifecycle. Over the years, various automated tools have been...
Md Rafi Ur Rashid, MD Sadik Hossain Shanto, Vishnu Asutosh Dasu +1 more
Vision-Language Models (VLMs) are now a core part of modern AI. Recent work proposed several visual jailbreak attacks using single/ holistic images....
Minbeom Kim, Mihir Parmar, Phillip Wallis +5 more
AI agents equipped with tool-calling capabilities are susceptible to Indirect Prompt Injection (IPI) attacks. In this attack scenario, malicious...
Yuhao Wang, Shengfang Zhai, Guanghao Jin +3 more
Large Language Model (LLM)-based agents employ external and internal memory systems to handle complex, goal-oriented tasks, yet this exposes them to...
Zhiyu Sun, Minrui Luo, Yu Wang +2 more
Large language models (LLMs) are pretrained on corpora containing trillions of tokens and, therefore, inevitably memorize sensitive information....
Ruoyao Wen, Hao Li, Chaowei Xiao +1 more
Indirect prompt injection threatens LLM agents by embedding malicious instructions in external content, enabling unauthorized actions and data theft....
Abdullah Arafat Miah, Kevin Vu, Yu Bi
Spiking Neural Networks (SNNs) are energy-efficient counterparts of Deep Neural Networks (DNNs) with high biological plausibility, as information is...
Shang Liu, Hanyu Pei, Zeyan Liu
Large Language Models(LLMs) have been successful in numerous fields. Alignment has usually been applied to prevent them from harmful purposes....
Zhuoheng Li, Ying Chen
Multimodal large language models (MLLMs) have advanced the capabilities to interpret and act on visual input in 3D environments, empowering diverse...
Mingqian Feng, Xiaodong Liu, Weiwei Yang +4 more
Multi-turn jailbreaks capture the real threat model for safety-aligned chatbots, where single-turn attacks are merely a special case. Yet existing...
Yassine Chagna, Antal Goldschmidt
This project explores large language models (LLMs) for anomaly detection across heterogeneous log sources. Traditional intrusion detection systems...
Fengpeng Li, Kemou Li, Qizhou Wang +2 more
Concept erasure helps stop diffusion models (DMs) from generating harmful content; but current methods face robustness retention trade off....
Ying Song, Balaji Palanisamy
Graph-structured data underpin a wide spectrum of modern applications. However, complex graph topologies and homophilic patterns can facilitate...
Mengyao Du, Han Fang, Haokai Ma +4 more
Suffix-based jailbreak attacks append an adversarial suffix, i.e., a short token sequence, to steer aligned LLMs into unsafe outputs. Since suffixes...
Haipeng Li, Rongxuan Peng, Anwei Luo +3 more
The rapid advancement of AI-Generated Content (AIGC) technologies poses significant challenges for authenticity assessment. However, existing...
Minkyoo Song, Jaehan Kim, Myungchul Kang +3 more
Graph-based retrieval-augmented generation (Graph RAG) is increasingly deployed to support LLM applications by augmenting user queries with...
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