1 citations · 1 across the 2 of their papers we have counts for
6 papers
The Attack and Defense Landscape of Agentic AI: A Comprehensive Survey
Juhee Kim, Xiaoyuan Liu, Zhun Wang +4
AI agents that combine large language models with non-AI system components are rapidly emerging in real-world applications, offering unprecedented automation and flexibility. Howev…
RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content
Zhuowen Yuan, Zidi Xiong, Yi Zeng +4
Recent advancements in Large Language Models (LLMs) have showcased remarkable capabilities across various tasks in different domains. However, the emergence of biases and the poten…
C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models
Mintong Kang, Nezihe Merve Gürel, Ning Yu +2
Despite the impressive capabilities of large language models (LLMs) across diverse applications, they still suffer from trustworthiness issues, such as hallucinations and misalignm…
GRATH: Gradual Self-Truthifying for Large Language Models
Weixin Chen, Dawn Song, Bo Li
Truthfulness is paramount for large language models (LLMs) as they are increasingly deployed in real-world applications. However, existing LLMs still struggle with generating truth…
TextGuard: Provable Defense against Backdoor Attacks on Text Classification
Hengzhi Pei, Jinyuan Jia, Wenbo Guo +2
Backdoor attacks have become a major security threat for deploying machine learning models in security-critical applications. Existing research endeavors have proposed many defense…
DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification
Mintong Kang, Dawn Song, Bo Li
Diffusion-based purification defenses leverage diffusion models to remove crafted perturbations of adversarial examples and achieve state-of-the-art robustness. Recent studies show…