2,077+ academic papers on AI security, attacks, and defenses
Showing 121–140 of 522 papers
Clear filtersAbdullah 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...
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...
Sung-Hoon Yoon, Ruizhi Qian, Minda Zhao +2 more
Large Language Models (LLMs) have become integral to many domains, making their safety a critical priority. Prior jailbreaking research has explored...
Xin Chen, Jie Zhang, Florian Tramèr
Prompt injection is one of the most critical vulnerabilities in LLM agents; yet, effective automated attacks remain largely unexplored from an...
Takashi Koide, Hiroki Nakano, Daiki Chiba
Phishing sites continue to grow in volume and sophistication. Recent work leverages large language models (LLMs) to analyze URLs, HTML, and rendered...
Yao Zhou, Zeen Song, Wenwen Qiang +4 more
Safety alignment mechanisms in Large Language Models (LLMs) often operate as latent internal states, obscuring the model's inherent capabilities....
Zihan Wang, Hongwei Li, Rui Zhang +2 more
Chat template is a common technique used in the training and inference stages of Large Language Models (LLMs). It can transform input and output data...
Ziyou Jiang, Lin Shi, Guowei Yang +3 more
Cyber attacks have become a serious threat to the security of software systems. Many organizations have built their security knowledge bases to...
Yunbei Zhang, Yingqiang Ge, Weijie Xu +3 more
Current multimodal red teaming treats images as wrappers for malicious payloads via typography or adversarial noise. These attacks are structurally...
Ethan Rathbun, Wo Wei Lin, Alina Oprea +1 more
Simulated environments are a key piece in the success of Reinforcement Learning (RL), allowing practitioners and researchers to train decision making...
Jafar Isbarov, Murat Kantarcioglu
As AI agents automate critical workloads, they remain vulnerable to indirect prompt injection (IPI) attacks. Current defenses rely on monitoring...
Joachim Schaeffer, Arjun Khandelwal, Tyler Tracy
Future AI deployments will likely be monitored for malicious behaviour. The ability of these AIs to subvert monitors by adversarially selecting...
Jaehyun Kwak, Nam Cao, Boryeong Cho +3 more
Adversarial attacks against Large Vision-Language Models (LVLMs) are crucial for exposing safety vulnerabilities in modern multimodal systems. Recent...
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert