2,077+ academic papers on AI security, attacks, and defenses
Showing 21–40 of 79 papers
Clear filtersJunhyeok Lee, Han Jang, Kyu Sung Choi
Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems are increasingly integrated into clinical workflows; however, prompt...
Hao Li, Ruoyao Wen, Shanghao Shi +2 more
AI agents that autonomously interact with external tools and environments show great promise across real-world applications. However, the external...
Yunpeng Xiong, Ting Zhang
Static Application Security Testing (SAST) tools are essential for identifying software vulnerabilities, but they often produce a high volume of...
Ivan K. Tung, Yu Xiang Shi, Alex Chien +2 more
Creating attack paths for cyber defence exercises requires substantial expert effort. Existing automation requires vulnerability graphs or exploit...
Miao Lin, Feng Yu, Rui Ning +6 more
Deep neural networks are highly susceptible to backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the pervasive...
Thomas Heverin
Prompt injection evaluations typically treat refusal as a stable, binary indicator of safety. This study challenges that paradigm by modeling refusal...
Zelong Zheng, Jiayuan Zhou, Xing Hu +2 more
Software vulnerability management has become increasingly critical as modern systems scale in size and complexity. However, existing automated...
Fan Huang, Haewoon Kwak, Jisun An
Large Language Models (LLMs) are increasingly employed in various question-answering tasks. However, recent studies showcase that LLMs are...
Jiayi Yuan, Jonathan Nöther, Natasha Jaques +1 more
While recent automated red-teaming methods show promise for systematically exposing model vulnerabilities, most existing approaches rely on...
Yow-Fu Liou, Yu-Chien Tang, Yu-Hsiang Liu +1 more
Benchmarking large language models (LLMs) is critical for understanding their capabilities, limitations, and robustness. In addition to interface...
Chutian Huang, Dake Cao, Jiacheng Ji +3 more
Background: While Large Language Models (LLMs) have achieved widespread adoption, malicious prompt engineering specifically "jailbreak attacks" poses...
Haoze Guo, Ziqi Wei
Retrieval-augmented generation (RAG) systems put more and more emphasis on grounding their responses in user-generated content found on the Web,...
Shaznin Sultana, Sadia Afreen, Nasir U. Eisty
Context: Traditional software security analysis methods struggle to keep pace with the scale and complexity of modern codebases, requiring...
Quy-Anh Dang, Chris Ngo, Truong-Son Hy
As large language models (LLMs) become integral to safety-critical applications, ensuring their robustness against adversarial prompts is paramount....
Zejian Chen, Chaozhuo Li, Chao Li +3 more
This paper provides a systematic survey of jailbreak attacks and defenses on Large Language Models (LLMs) and Vision-Language Models (VLMs),...
Xiangzhe Yuan, Zhenhao Zhang, Haoming Tang +1 more
As LLMs gain persuasive agentic capabilities through extended dialogues, they introduce novel risks in multi-turn conversational scams that...
Songyang Liu, Chaozhuo Li, Rui Pu +5 more
Jailbreak attacks present a significant challenge to the safety of Large Language Models (LLMs), yet current automated evaluation methods largely...
Md Hasan Saju, Maher Muhtadi, Akramul Azim
The rapid advancement of Large Language Models (LLMs) presents new opportunities for automated software vulnerability detection, a crucial task in...
Jingyu Zhang
Customer-service LLM agents increasingly make policy-bound decisions (refunds, rebooking, billing disputes), but the same ``helpful'' interaction...
Manu, Yi Guo, Kanchana Thilakarathna +5 more
Large Language Models (LLMs) can be driven into over-generation, emitting thousands of tokens before producing an end-of-sequence (EOS) token. This...
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