55 citations · 121 across the 10 of their papers we have counts for
6 papers · 1 filter
SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner
Xunguang Wang, Daoyuan Wu, Zhenlan Ji +7
Jailbreaking is an emerging adversarial attack that bypasses the safety alignment deployed in off-the-shelf large language models (LLMs) and has evolved into multiple categories: h…
PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation
Ye Liu, Yue Xue, Daoyuan Wu +4
With recent advances in large language models (LLMs), this paper explores the potential of leveraging state-of-the-art LLMs,such as GPT-4, to transfer existing human-written proper…
Static Application Security Testing (SAST) Tools for Smart Contracts: How Far Are We?
Kaixuan Li, Yue Xue, Sen Chen +6
In recent years, the importance of smart contract security has been heightened by the increasing number of attacks against them. To address this issue, a multitude of static applic…
LLMs Can Defend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper
Daoyuan Wu, Shuai Wang, Yang Liu +1
Jailbreaking is an emerging adversarial attack that bypasses the safety alignment deployed in off-the-shelf large language models (LLMs). A considerable amount of research exists p…
Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications
Wei Ma, Daoyuan Wu, Yuqiang Sun +5
Smart contracts are decentralized applications built atop blockchains like Ethereum. Recent research has shown that large language models (LLMs) have potential in auditing smart co…
LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning
Yuqiang Sun, Daoyuan Wu, Yue Xue +5
Large language models (LLMs) have demonstrated significant potential in various tasks, including those requiring human-level intelligence, such as vulnerability detection. However,…