1 st ACM Workshop on
Large AI Systems and Models with Privacy and Safety Analysis
October 14, 2024 — Salt Lake City, U.S.A.
co-located with the 31st ACM Conference on Computer and Communications Security

Program (Tentative) - Oct 14, 2024

Full-day Workshop

The following times are on local time zone.

9:20–9:30 Opening Remarks
9:30–10:30 Keynote Speech 1: Ben Zhao （Professor, University of Chicago）
10:30–11:00 Morning Coffee Break
11:00–11:30 Session I: Cybersecurity Threat Intelligence
11:00: ThreatKG: An AI-Powered System for Automated Online Threat Intelligence
Peng Gao (Virginia Tech), Xiaoyuan Liu (University of California, Berkeley), Edward Choi (University of California, Berkeley), Sibo Ma (University of California, Berkeley), Xinyu Yang (Virginia Tech), and Dawn Song (University of California, Berkeley)
11:10: Mitigating Unauthorized Speech Synthesis for Voice-Activated Systems
Zhisheng Zhang (Beijing University of Posts and Telecommunications), Qianyi Yang (Beijing University of Posts and Telecommunications), Derui Wang (CSIRO's Data61), Pengyang Huang (Beijing University of Posts and Telecommunications), Yuxin Cao (National University of Singapore), Kai Ye (The University of Hong Kong), and Jie Hao (Beijing University of Posts and Telecommunications)
11:20: How to Efficiently Manage Critical Infrastructure Vulnerabilities? Toward Large Code-graph Models
Hongying Zhang (Shanghai Jiao Tong University), Gaolei Li (Shanghai Jiao Tong University), Shenghong Li (Shanghai Jiao Tong University), Hongfu Liu (Shanghai Jiao Tong University), Shuo Wang (Shanghai Jiao Tong University), and Jianhua Li (Shanghai Jiao Tong University)
11:30–12:00 Session II: Adversarial Attacks and Robustness
11:30: Adversarial Attacks to Multi-Modal Models
Zhihao Dou (Duke University), Xin Hu (The University of Tokyo), Haibo Yang (Rochester Institute of Technology), Zhuqing Liu (The Ohio State University), and Minghong Fang (Duke University)
11:40: TrojFair: Trojan Fairness Attacks
Jiaqi Xue (University of Central Florida), Mengxin Zheng (University of Central Florida), Yi Sheng (George Mason University), Lei Yang (George Mason University), Qian Lou (University of Central Florida), and Lei Jiang (Indiana University Bloomington)
11:50: PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
Kaijie Zhu (Institute of Automation, Chinese Academy of Sciences), Jindong Wang (Microsoft Research), Jiaheng Zhou (Institute of Automation, Chinese Academy of Sciences), Zichen Wang (Institute of Automation, Chinese Academy of Sciences), Hao Chen (Carnegie Mellon University), Yidong Wang (Peking University), Linyi Yang (Westlake University), Wei Ye (Peking University), Yue Zhang (Westlake University), Neil Gong (Duke University), and Xing Xie (Microsoft)
12:00–14:00 Lunch
14:00–15:00 Keynote Speech 2: Chaowei Xiao （NVIDIA and University of Wisconsin, Madison）
15:00–15:30 Afternoon Coffee Break
15:30–16:00 Session III: Large Language Model Security
15:30: Have You Merged My Model? On The Robustness of Merged Machine Learning Models
Tianshuo Cong (Tsinghua University), Delong Ran (Tsinghua University), Zesen Liu (Xidian University), Xinlei He (The Hong Kong University of Science and Technology (Guangzhou)), Jinyuan Liu (Tsinghua University), Yichen Gong (Tsinghua University), Qi Li (Tsinghua University), Anyu Wang (Tsinghua University), and Xiaoyun Wang (Tsinghua University)
15:40: "Prompter Says": A Linguistic Approach to Understanding and Detecting Jailbreak Attacks Against Large-Language Models
Dylan Lee (University of California, Irvine), Shaoyuan Xie (University of California, Irvine), Shagoto Rahman (University of California, Irvine), Kenneth Pat (University of California, Irvine), David Lee (University of California, Irvine), and Qi Alfred Chen (University of California, Irvine)
15:50: Towards Large Language Model (LLM) Forensics Using Feature Extraction
Maxim Chernyshev (Deakin University), Zubair Baig (Deakin University), and Robin Ram Mohan Doss (Deakin University)
16:00–16:20 Session IV: Secure Learning and Model Attribution
16:00: CryptoTrain: Fast Secure Training on Encrypted Data
Jiaqi Xue (University of Central Florida), Yancheng Zhang (University of Central Florida), Yanshan Wang (University of Pittsburgh), Xueqiang Wang (University of Central Florida), Hao Zheng (University of Central Florida), and Qian Lou (University of Central Florida)
16:10: Detection and Attribution of Diffusion Model of Character Animation Based on Spatio-Temporal Attention
Fazhong Liu (Shanghai Jiao Tong University), Yan Meng (Shanghai Jiao Tong University), Tian Dong (Shanghai Jiao Tong University), Guoxing Chen (Shanghai Jiao Tong University), and Haojin Zhu (Shanghai Jiao Tong University)
16:20–16:30 Concluding Remarks

Call for Papers

Important Dates

  • Paper and talk submission deadline: July 18th, 2024, 11:59 PM (all deadlines are AoE, UTC-12)
  • Acceptance notification: August 14th, 2024
  • Camera ready due: September 8th, 2024
  • Workshop day: October 14th, 2024

Overview

As Large AI Systems and Models (LAMs) become increasingly pivotal in a wide array of applications, their potential impact on the privacy and cybersecurity of critical infrastructure becomes a pressing concern. LAMPS is dedicated to addressing these unique challenges, fostering a dialogue on the latest advancements and ethical considerations in enhancing the privacy and cybersecurity of LAMs, particularly in the context of critical infrastructure protection.

LAMPS will bring together global experts to dissect the nuanced privacy and cybersecurity challenges posed by LAMs, especially in critical infrastructure sectors. This workshop will serve as a platform to unveil novel techniques, share best practices, and chart the course for future research, with a special emphasis on the delicate balance between advancing AI technologies and securing critical digital and physical systems.

Topics of Interest

Topics of interest include (but are not limited to):

Secure Large AI Systems and Models for Critical Infrastructure

  • AI-Enhanced Threat Intelligence and Detection
  • Automated Security Orchestration and Incident Response
  • Large AI Models in Vulnerability Assessment and Penetration Testing
  • AI-Driven Network Security Management
  • AI-Enabled Security Awareness and Education
  • Collaborative AI for Global Cyber Threat Intelligence Sharing
  • Regulatory Compliance and AI in Cybersecurity

Large AI Systems and Models' Privacy and Security Vulnerabilities

  • Advanced Threat Landscape
  • Holistic Security and Privacy Frameworks
  • Innovations in Privacy Preservation
  • Secure Computation in AI

Data Anonymization and Synthetic Data

  • Advancements in Data Protection
  • Cross-Border Data Flow and Cooperation
  • Intellectual Property Protection
  • Combatting Deepfakes

Human-Centric Large AI Systems and Models

  • User Vulnerability and Defense Mechanisms
  • Equity and Inclusivity in AI
  • Participative Large AI Governance
  • Enhancing Explainability and Trust
  • Designing for Security and Usability
  • Ethics and Decision-Making in AI
  • Frameworks for Responsible AI Governance

Submission Guidelines

Submitted papers must not substantially overlap with papers that have been published or simultaneously submitted to a journal or a conference with proceedings. Short submissions should be at most 4 pages in the ACM double-column format. Submissions should be at most 10 pages in the ACM double-column format, excluding well-marked appendices, and at most 12 pages in total. Systematization of knowledge (SoK) submissions could be at most 15 pages long, excluding well-marked appendices, and at most 17 pages. Submissions are not required to be anonymized.

Submission Site

Submission link: https://ccs24-lamps.hotcrp.com

Only PDF files will be accepted. Submissions not meeting these guidelines risk rejection without consideration of their merits. Authors of accepted papers must guarantee that one of the authors will register and present the paper at the workshop. Proceedings of the workshop will be available on a CD to the workshop attendees and will become part of the ACM Digital Library.

The archival papers will be included in the workshop proceedings. Due to time constraints, accepted papers will be selected for presentation as either talk or poster based on their review score and novelty. Nonetheless, all accepted papers should be considered as having equal importance.

Authors are responsible for obtaining appropriate publication clearances. Attendance and presentation by at least one author of each accepted paper at the workshop are mandatory for the paper to be included in the proceedings.

For any questions, please contact one of the workshop organizers at jason.xue@data61.csiro.au or wangshuosj@sjtu.edu.cn .

Committee

Workshop Chairs

Program Committee

Chong Xiang Princeton University United States of America
Derui Wang CSIRO's Data61 Australia
Giovanni Apruzzese University of Liechtenstein Liechtenstein
Jamie Hayes Google Deepmind United Kingdom
Jinyuan Jia The Pennsylvania State University United States of America
Konrad Rieck TU Berlin Germany
Kristen Moore CSIRO's Data61 Australia
Mainack Mondal Indian Institute of Technology, Kharagpur India
Mathias Humbert University of Lausanne Switzerland
Minghong Fang Duke University United States of America
Peng Gao Virginia Tech United States of America
Pin-Yu Chen IBM Research United States of America
Sagar Samtani Indiana University United States of America
Sai Teja Peddinti Google United States of America
Shiqing Ma University of Massachusetts Amherst United States of America
Shuang Hao University of Texas at Dallas United States of America
Stjepan Picek Radboud University Netherlands
Tian Dong Shanghai Jiao Tong University China
Tianshuo Cong Tsinghua University China
Torsten Krauß University of Wuerzburg Germany
Varun Chandrasekaran University of Illinois Urbana-Champaign United States of America
Xiaoning Du Monash University Australia
Xinlei He The Hong Kong University of Science and Technology (Guangzhou) China
Yanjiao Chen Zhejiang University China
Yinzhi Cao Johns Hopkins University United States of America