2,529+ academic papers on AI security, attacks, and defenses
Showing 41–60 of 867 papers
Clear filtersZi Li, Tian Zhou, Wenze Li +3 more
Local fine-tuning datasets routinely contain sensitive secrets such as API keys, personal identifiers, and financial records. Although ''local...
Soheil Khodayari, Xuenan Zhang, Bhupendra Acharya +1 more
As LLMs are increasingly integrated into systems that browse, retrieve, summarize, and act on web content, webpages have become an untrusted input...
Benjamin Probst, Andreas Happe, Jürgen Cito
Recent research has demonstrated the potential of Large Language Models (LLMs) for autonomous penetration testing, particularly when using...
Weiyi Kong, Ahmad Mohammad Saber, Amr Youssef +1 more
In modern energy systems, industrial control systems (ICS) and power-system SCADA require intrusion detection that is not only accurate but also...
Shirin Alanova, Bogdan Minko, Sabrina Sadiekh +1 more
Safety mechanisms for large language models (LLMs) remain predominantly English-centric, creating systematic vulnerabilities in multilingual...
Zeming Dong, Yuejun Guo, Qiang Hu +5 more
Source code and its accompanying comments are complementary yet naturally aligned modalities-code encodes structural logic while comments capture...
Mohamed Taoufik Kaouthar El Idrissi, Edward Zulkoski, Mohammad Hamdaqa
Code understanding models increasingly rely on pretrained language models (PLMs) and graph neural networks (GNNs), which capture complementary...
Mengyao Du, Han Fang, Haokai Ma +4 more
Web agents have emerged as an effective paradigm for automating interactions with complex web environments, yet remain vulnerable to prompt injection...
Miles Q. Li, Benjamin C. M. Fung, Boyang Li +2 more
Existing white-box jailbreak attacks against aligned LLMs typically append discrete adversarial suffixes to the user prompt, which visibly alters the...
Allen Jue
Learned index structures achieve high performance by modeling the cumulative distribution function (CDF) of keys, but this reliance on data...
Zonghao Ying, Haozheng Wang, Jiangfan Liu +5 more
Large Language Model (LLM) agents are increasingly used to automate complex workflows, but integrating untrusted external data with privileged...
Xinhe Wang, Katia Sycara, Yaqi Xie
Large (vision-)language models exhibit remarkable capability but remain highly susceptible to jailbreaking. Existing safety training approaches aim...
Priyal Deep, Shane Emmons, Amy Fox +3 more
LLM-powered applications routinely embed secrets in system prompts, yet models can be tricked into revealing them. We built an adaptive attacker that...
Yu Cui, Ruiqing Yue, Hang Fu +6 more
With the wide adoption of personal AI assistants such as OpenClaw, privacy leakage in user interaction contexts with large language model (LLM)...
Yuchuan Zhao, Tong Chen, Junliang Yu +3 more
Large language model-powered sequential recommender systems (LLM-SRSs) have recently demonstrated remarkable performance, enabling recommendations...
Naheed Rayhan, Sohely Jahan
Large language models (LLMs) are increasingly integrated into sensitive workflows, raising the stakes for adversarial robustness and safety. This...
Zihan Wang, Rui Zhang, Yu Liu +4 more
LLM agents increasingly rely on skills to encapsulate reusable capabilities via progressively disclosed instructions. High-quality skills inject...
Jiali Wei, Ming Fan, Guoheng Sun +3 more
The growing application of large language models (LLMs) in safety-critical domains has raised urgent concerns about their security. Many recent...
Run Hao, Zhuoran Tan
Model Context Protocol (MCP) is increasingly adopted for tool-integrated LLM agents, but its multi-layer design and third-party server ecosystem...
Zhaohui Geoffrey Wang
Automated code vulnerability detection is critical for software security, yet existing approaches face a fundamental trade-off between detection...
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