LLM Red Teaming

LLM: Large Language Model

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3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Latest papers 47

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How Far Will They Go? Red-Teaming Online Influence with Large Language Models
May 20, 2026Daniel C. Ruiz, Anna Serbina, Ashwin Rao +2Language Model Steering LLM Auditing
Information Sciences Institute University of Southern California Los Angeles, CA, USA
Model-Agnostic Lifelong LLM Safety via Externalized Attack-Defense Co-Evolution
May 13, 2026Xiaozhe Zhang, Chaozhuo Li, Hui Liu +4Continual Learning for LLMs Adversarial Attacks on LLMs
City University of Hong Kong · Beijing University of Posts and Telecommunications · Wuhan University +2
Metis: Learning to Jailbreak LLMs via Self-Evolving Metacognitive Policy Optimization
May 11, 2026Huilin Zhou, Jian Zhao, Yilu Zhong +7Inference-Time Optimization Adversarial Attacks on LLMs
University of Science and Technology of China · Institute of Artificial Intelligence (TeleAI), China Telecom
OTora: A Unified Red Teaming Framework for Reasoning-Level Denial-of-Service in LLM Agents
May 9, 2026Xinyu Li, Ronghui Mu, Lin Li +2Adversarial Attacks on LLMs LLM Red Teaming
Department of Computer Science, University of Exeter · Department of Computer Science, University of Oxford · Department of Mathematics and Computer Science, Eindhoven University of Technology
LoopTrap: Termination Poisoning Attacks on LLM Agents
May 7, 2026Huiyu Xu, Zhibo Wang, Wenhui Zhang +4Adversarial Attacks on LLMs LLM Red Teaming
The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou, Zhejiang, P. R. China · School of Cyber Science and Engineering Southeast University Nanjing, Jiangsu, P. R. China
PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI
May 7, 2026Wesley Hanwen Deng, Mingxi Yan, Sunnie S. Y. Kim +5Adversarial Prompt Generation LLM Red Teaming
Carnegie Mellon University Pittsburgh, Pennsylvania, USA · Apple Seattle, Washington, USA · Apple Cupertino, California, USA +1
Redefining AI Red Teaming in the Agentic Era: From Weeks to Hours
May 5, 2026Raja Sekhar Rao Dheekonda, Will Pearce, Nick LandersAdversarial Attacks on LLMs LLM Red Teaming
1Dreadnode, USA
ContextualJailbreak: Evolutionary Red-Teaming via Simulated Conversational Priming
May 4, 2026Mario Rodríguez Béjar, Francisco J. Cortés-Delgado, S. Braghin +1Evolutionary Optimization LLM Red Teaming
Universidad de Murcia · IBM Research
Stable-GFlowNet: Toward Diverse and Robust LLM Red-Teaming via Contrastive Trajectory Balance
May 1, 2026Minchan Kwon, Sunghyun Baek, Minseo Kim +3Language Model Safety Evaluation Adversarial Attacks on LLMs
1KAIST · 2Naver AI Lab.
Training a General Purpose Automated Red Teaming Model
Apr 24, 2026Aishwarya Padmakumar, Leon Derczynski, Traian Rebedea +1Adversarial Attacks on LLMs LLM Red Teaming
NVIDIA
Adaptive Instruction Composition for Automated LLM Red-Teaming
Apr 22, 2026Jesse Zymet, Andy Luo, Swapnil Shinde +2Adversarial Prompt Generation LLM Red Teaming
Capital One, AI Foundations
ARES: Adaptive Red-Teaming and End-to-End Repair of Policy-Reward System
Apr 20, 2026Jiacheng Liang, Yao Ma, Tharindu Kumarage +5Reward Modeling Adversarial Attacks on LLMs
Stony Brook University · Amazon Nova Responsible AI
T-MAP: Red-Teaming LLM Agents with Trajectory-aware Evolutionary Search
Mar 21, 2026Hyomin Lee, Sangwoo Park, Yumin Choi +3Adversarial Prompt Generation LLM Red Teaming
KAIST · University of California, Los Angeles
Red-Teaming Coding Agents from a Tool-Invocation Perspective: An Empirical Security Assessment
Sep 6, 2025Yuchong Xie, Mingyu Luo, Zesen Liu +7Prompt Injection AI Coding Agents
The Hong Kong University of Science and Technology, China · Fudan University, China
Addressing Benchmarking Gaps in Large Language Models for Health and Medicine with Dynamic Red-Teaming
Jul 30, 2025Jiazhen Pan, Bailiang Jian, Paul Hager +20LLM Safety Benchmarks Language Model Safety Evaluation
Cherise · School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany · Department of Engineering Science, University of Oxford, Oxford, United Kingdom +9
RedCoder: Automated Multi-Turn Red Teaming for Code LLMs
Jun 25, 2025Wenjie Jacky Mo, Qin Liu, Xiaofei Wen +5Adversarial Prompt Generation LLM Red Teaming
University of California, Davis · University of Southern California
Learning diverse attacks on large language models for robust red-teaming and safety tuning
May 28, 2024Seanie Lee, Minsu Kim, Lynn Cherif +8Adversarial Attacks on LLMs LLM Red Teaming
KAIST · Mila – Québec AI Institute · Université de Montréal +3

May 20, 2026, cs.CLJ/K move · Enter open · S save

How Far Will They Go? Red-Teaming Online Influence with Large Language Models

Daniel C. Ruiz, Anna Serbina, Ashwin Rao, Emilio Ferrara+1

Information Sciences Institute University of Southern California Los Angeles, CA, USA

Language Model Steering LLM Auditing Adversarial Attacks on LLMs Political Bias in Language Models LLM Red Teaming

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As large language model (LLM)-based agents increasingly participate in online discourse, red-teaming their capacity to support political influence campaigns is critical for information integrity. In pursuit of this goal, we focus on locally deployed open-source LLMs, as opposed to frontier API-only models, given their superior alignment with the operational constraints of privacy-conscious malicious actors deployed in social media environments. We introduce an empirical red-teaming framework for measuring LLM Overton Windows (OWs), defined as the range of political opinions a model can reliably express on controversial topics, and for quantifying how simple natural-language jailbreaks expand that range. We evaluate more than 30 LLMs spanning 10 model families and five countries of origin. We find systematic asymmetries in political expressivity: open-source LLMs are typically more willing to generate left-leaning social media content, OWs tend to contract inversely to model size, and regional differences are substantial despite uneven representation in the open-source ecosystem. Jailbreak potency also varies sharply across model families, motivating a workflow for identifying effective combinations of jailbreak techniques. Taken together, our results establish a practical framework for auditing the political steerability of open-source LLMs and for helping future researchers design stronger countermeasures against LLM-enabled influence campaigns.

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Latest papers 47