Jiangyi Deng (Zhejiang University), Xinfeng Li (Zhejiang University), Yanjiao Chen (Zhejiang University), Yijie Bai (Zhejiang University), Haiqin Weng (Ant Group), Yan Liu (Ant Group), Tao Wei (Ant Group), Wenyuan Xu (Zhejiang University)

Malicious shell commands are linchpins to many cyber-attacks, but may not be easy to understand by security analysts due to complicated and often disguised code structures. Advances in large language models (LLMs) have unlocked the possibility of generating understandable explanations for shell commands. However, existing general-purpose LLMs suffer from a lack of expert knowledge and a tendency to hallucinate in the task of shell command explanation. In this paper, we present Raconteur, a knowledgeable, expressive and portable shell command explainer powered by LLM. Raconteur is infused with professional knowledge to provide comprehensive explanations on shell commands, including not only what the command does (i.e., behavior) but also why the command does it (i.e., purpose). To shed light on the high-level intent of the command, we also translate the natural-language-based explanation into standard technique & tactic defined by MITRE ATT&CK, the worldwide knowledge base of cybersecurity. To enable Raconteur to explain unseen private commands, we further develop a documentation retriever to obtain relevant information from complementary documentations to assist the explanation process. We have created a large-scale dataset for training and conducted extensive experiments to evaluate the capability of Raconteur in shell command explanation. The experiments verify that Raconteur is able to provide high-quality explanations and in-depth insight of the intent of the command.

View More Papers

Iris: Dynamic Privacy Preserving Search in Authenticated Chord Peer-to-Peer...

Angeliki Aktypi (University of Oxford), Kasper Rasmussen (University of Oxford)

Read More

Rondo: Scalable and Reconfiguration-Friendly Randomness Beacon

Xuanji Meng (Tsinghua University), Xiao Sui (Shandong University), Zhaoxin Yang (Tsinghua University), Kang Rong (Blockchain Platform Division,Ant Group), Wenbo Xu (Blockchain Platform Division,Ant Group), Shenglong Chen (Blockchain Platform Division,Ant Group), Ying Yan (Blockchain Platform Division,Ant Group), Sisi Duan (Tsinghua University)

Read More

Generating API Parameter Security Rules with LLM for API...

Jinghua Liu (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Yi Yang (Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China; School of Cyber Security, University of Chinese Academy of Sciences, China), Kai Chen (Institute of Information Engineering, Chinese Academy of…

Read More

Automatic Library Fuzzing through API Relation Evolvement

Jiayi Lin (The University of Hong Kong), Qingyu Zhang (The University of Hong Kong), Junzhe Li (The University of Hong Kong), Chenxin Sun (The University of Hong Kong), Hao Zhou (The Hong Kong Polytechnic University), Changhua Luo (The University of Hong Kong), Chenxiong Qian (The University of Hong Kong)

Read More