2,529+ academic papers on AI security, attacks, and defenses
Showing 1–10 of 10 papers
Clear filtersJoel Rorseth, Parke Godfrey, Lukasz Golab +2 more
This paper demonstrates RUBEN, an interactive tool for discovering minimal rules to explain the outputs of retrieval-augmented large language models...
Michael A. Riegler, Inga Strümke
We present swarm-attack, an open-source adversarial testing framework in which multiple lightweight LLM agents coordinate through shared memory,...
Chengjie Wang, Jingzheng Wu, Xiang Ling +2 more
Large language models (LLMs) are now largely involved in software development workflows, and the code they generate routinely includes third-party...
Kerri Prinos, Lilianne Brush, Cameron Denton +5 more
Agentic systems involved in high-stake decision-making under adversarial pressure need formal guarantees not offered by existing approaches....
Mingming Zha, Xiaofeng Wang
Autonomous LLM agents operate as long-running processes with persistent workspaces, memory files, scheduled task state, and messaging integrations....
Neha Nagaraja, Hayretdin Bahsi, Carlo R. da Cunha
As large language models are integrated into autonomous robotic systems for task planning and control, compromised inputs or unsafe model outputs can...
Kato Mivule
This paper extends the Classification Error Gauge (x-CEG) framework, originally developed for measuring the privacy-utility trade-off in tabular...
Mikko Lempinen, Joni Kemppainen, Niklas Raesalmi
As artificial intelligence (AI) systems are increasingly deployed across critical domains, their security vulnerabilities pose growing risks of...
Yuan Fang, Yiming Luo, Aimin Zhou +1 more
Ensuring the safety of large language models (LLMs) requires robust red teaming, yet the systematic synthesis of high-quality toxic data remains...
Shangkun Che, Silin Du, Ge Gao
The widespread use of Large Language Models (LLMs) in text generation has raised increasing concerns about intellectual property disputes....
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