Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· 0 citations
TL;DR
The 2nd SeT-LLM Workshop on Secure and Trustworthy Large Language Models brings together researchers and practitioners from data mining, machine learning, security, and responsible AI to address issues from a data-centric, system-level perspective.
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 data pipelines and affect downstream decisions. The rise of LLM-based agents further amplifies these risks through unsafe tool use and autonomous decision-making. The 2nd SeT-LLM Workshop on Secure and Trustworthy Large Language Models brings together researchers and practitioners from data mining, machine learning, security, and responsible AI to address these issues from a data-centric, system-level perspective, spanning robust defenses, trustworthy evaluation, privacy and copyright protection, robustness, alignment and safety, agent security, and high-stakes applications. Through invited talks, contributed papers, a poster session, and a panel discussion, the workshop prioritizes early-stage ideas, system experiences, and open problems across the lifecycle of LLM-based systems.
This survey offers a comprehensive overview of the main data security risks facing LLMs and reviews current defense strategies, including adversarial training, data cleaning, output guardrails, Reinforcement Learning from Human Feedback, data augmentation, and Retrieval-Augmented Generation (RAG)/agent defenses.
Kang Chen, Xiuze Zhou, Yuanhui Yu et al.· Journal of King Saud Univers...· 0 citations
A four-layer taxonomy mapping 13 vulnerability types across perception, brain, action, and interaction layers is contributed, and seven open problems centered on containment are identified.
Md Jafrin Hossain, Mohammad Arif Hossain, Nirwan Ansari· 0 citations
SecureBreak is introduced, a safety-oriented dataset designed to support the development of AI-driven solutions for detecting harmful LLM outputs caused by residual weaknesses in security alignment and is valuable not only for constructing post-generation filtering modules that act as a last-line defense, but also for building additional supervisory intelligence for alignment optimization.
Marco Arazzi, Vignesh Kumar Kembu, Antonino Nocera· Proceedings of the 15th Inte...· 0 citations
A role-layer framework is introduced to unify LLM risks and defenses, and identifies three key open challenges: moving from static detection accuracy to budgeted ecosystem-level risk evaluation, hardening LLM-centered verification pipelines against adversarial manipulation, and deploying auditable human-in-the-loop verification systems for trustworthy real-world misinformation defense.
Large language model (LLM) based agents are evolving from conversational chatbots into autonomous decision-makers that plan, reason, wield tools, and collaborate across high-stakes domains such as healthcare, finance, and scientific discovery. Yet this power brings a fundamental challenge: trustworthiness. How can we guarantee that an agent remains robust when real-world data shifts, degrades, or is deliberately poisoned? What defenses exist against memory injection, tool-based exploits, or cascade failures in multi-agent systems? Can we embed domain-specific causal validity, clinical safety, or fairness directly into agent reasoning? And how do we measure trust when it spans robustness, security, reliability, and alignment — each with its own irreconcilable trade-offs? This tutorial delivers a systematic, data-centric roadmap to build LLM agents that are not merely capable but provably trustworthy. We organize the landscape into four interconnected pillars: (i) generalizability under distribution shift, noise, and adversarial inputs; (ii) security architectures that defend against emerging threats — from indirect prompt injection to supply-chain vulnerabilities; (iii) domain-grounded trust in science, engineering, medicine, and commerce, where agents must respect theories, systems, clinical causality, and fairness constraints; and (iv) multi-dimensional evaluation benchmarks that expose trade-offs rather than collapsing them into a single score. By unifying advances in LLM agents, robust machine learning, and data-centric AI, we equip the audience with both foundational principles and actionable recipes to design, deploy, and ultimately trust the next generation of autonomous agent systems.
Tianlong Chen, Jian Pei, Minxing Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
This thesis proposes a unified two-layer approach to enhancing the trustworthiness and security of LLM-enabled systems and introduces the Adaptive LLM Threat Response (ALTR) framework, which integrates behavioral anomaly detection, context-aware prompt classification, and temporal threat memory to identify and mitigate adversarial interactions in real time.