paperAugust 2026UnreviewedOpen access

The 2nd SeT-LLM Workshop on Secure and Trustworthy Large Language Models

Lu Lin, Jinghui Chen, Ting Wang, Jieyu Zhao, Chaowei Xiao, Jian Kang, Michael Johnston

Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2

Abstract

Large language models (LLMs) are increasingly embedded as core components of data-centric systems, supporting analytical decision making, and automated reasoning over large-scale, heterogeneous datasets. Yet their deployment in open-world environments raises fundamental challenges to security and trustworthiness: LLMs can leak sensitive data, fall prey to prompt injection and jailbreaks, generate misinformation, and behave unpredictably under adversarial inputs, failures that propagate through d

Framework mappings

OWASP Top 10 for LLM Applications

LLM01Prompt Injection

MITRE ATLAS

AML.T0051LLM Prompt Injection
AML.T0054LLM Jailbreak

Suggested from the entry's categories.

Cite

@inproceedings{lin20262nd,
  title = {{The 2nd SeT-LLM Workshop on Secure and Trustworthy Large Language Models}},
  author = {Lu Lin and Jinghui Chen and Ting Wang and Jieyu Zhao and Chaowei Xiao and Jian Kang and Michael Johnston},
  year = {2026},
  month = aug,
  booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2},
  doi = {10.1145/3770855.3818247},
  url = {https://www.semanticscholar.org/paper/29d67405e278415b6e1ce58cab820c84cc22ee0c}
}