2,529+ academic papers on AI security, attacks, and defenses
Showing 61–80 of 969 papers
Clear filtersAllen Jue
Learned index structures achieve high performance by modeling the cumulative distribution function (CDF) of keys, but this reliance on data...
Mengnan Zhao, Lihe Zhang, Tianhang Zheng +2 more
Fast Adversarial Training (FAT) has attracted significant attention due to its efficiency in enhancing neural network robustness against adversarial...
Mengnan Zhao, Lihe Zhang, Bo Wang +3 more
Fast Adversarial Training (FAT) has proven effective in enhancing model robustness by encouraging networks to learn perturbation-invariant...
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...
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)...
Rong Xiang
Recent evidence suggests that frontier AI systems can exhibit agentic misalignment, generating and executing harmful actions derived from internally...
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...
Guilin Deng, Silong Chen, Yuchuan Luo +6 more
Federated Large Language Models (FedLLMs) enable multiple parties to collaboratively fine-tune LLMs without sharing raw data, addressing challenges...
Jesse Zymet, Andy Luo, Swapnil Shinde +2 more
Many approaches to LLM red-teaming leverage an attacker LLM to discover jailbreaks against a target. Several of them task the attacker with...
Irti Haq, Belén Saldías
As state-of-the-art Large Language Models (LLMs) have become ubiquitous, ensuring equitable performance across diverse demographics is critical....
Yannis Belkhiter, Giulio Zizzo, Sergio Maffeis +2 more
The growth of agentic AI has drawn significant attention to function calling Large Language Models (LLMs), which are designed to extend the...
Abhijit Talluri
Adversarial robustness evaluation underpins every claim of trustworthy ML deployment, yet the field suffers from fragmented protocols and undetected...
Nandakrishna Giri, Asmitha K. A., Serena Nicolazzo +2 more
Machine learning-based static malware detectors remain vulnerable to adversarial evasion techniques, such as metamorphic engine mutations. To address...
Pranav Pallerla, Wilson Naik Bhukya, Bharath Vemula +1 more
Retrieval-augmented generation (RAG) systems are increasingly deployed in sensitive domains such as healthcare and law, where they rely on private,...
MinJae Jung, YongTaek Lim, Chaeyun Kim +3 more
While Large Language Models (LLMs) are widely used, they remain susceptible to jailbreak prompts that can elicit harmful or inappropriate responses....
Hanrui Luo, Shreyank N Gowda
Detecting jailbreak behaviour in large language models remains challenging, particularly when strongly aligned models produce harmful outputs only...
Ruixuan Liu, David Evans, Li Xiong
Indistinguishability properties such as differential privacy bounds or low empirically measured membership inference are widely treated as proxies to...
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