- Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking
Zhicheng Fang, Jingjie Zheng, Chenxu Fu, Wei Xu · Feb 27, 2026 · Citations: 0
Red Team Llm As Judge Multi Agent
Jailbreak techniques for large language models (LLMs) evolve faster than benchmarks, making robustness estimates stale and difficult to compare across papers due to drift in datasets, harnesses, and judging protocols.
- What Matters For Safety Alignment?
Xing Li, Hui-Ling Zhen, Lihao Yin, Xianzhi Yu, Zhenhua Dong · Jan 7, 2026 · Citations: 0
Red Team Automatic Metrics Tool Use
This paper presents a comprehensive empirical study on the safety alignment capabilities.
- Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search
Xun Huang, Simeng Qin, Xiaoshuang Jia, Ranjie Duan, Huanqian Yan · Feb 26, 2026 · Citations: 0
Red Team Automatic Metrics
Owing to its conciseness and obscurity, classical Chinese can partially bypass existing safety constraints, exposing notable vulnerabilities in LLMs.
- MANATEE: Inference-Time Lightweight Diffusion Based Safety Defense for LLMs
Chun Yan Ryan Kan, Tommy Tran, Vedant Yadav, Ava Cai, Kevin Zhu · Feb 21, 2026 · Citations: 0
Red Team Automatic Metrics
We propose MANATEE, an inference-time defense that uses density estimation over a benign representation manifold.
- Contextualized Privacy Defense for LLM Agents
Yule Wen, Yanzhe Zhang, Jianxun Lian, Xiaoyuan Yi, Xing Xie · Mar 3, 2026 · Citations: 0
Simulation Env Long Horizon
LLM agents increasingly act on users' personal information, yet existing privacy defenses remain limited in both design and adaptability.
- "Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems
Xinfeng Li, Shenyu Dai, Kelong Zheng, Yue Xiao, Gelei Deng · Feb 24, 2026 · Citations: 0
Expert Verification Automatic Metrics
Large language model (LLM) agents are rapidly becoming trusted copilots in high-stakes domains like software development and healthcare.
- RLShield: Practical Multi-Agent RL for Financial Cyber Defense with Attack-Surface MDPs and Real-Time Response Orchestration
Srikumar Nayak · Feb 26, 2026 · Citations: 0
Automatic Metrics Multi Agent
This paper proposes RLShield, a practical multi-agent RL pipeline for financial cyber defense.
- ICON: Indirect Prompt Injection Defense for Agents based on Inference-Time Correction
Che Wang, Fuyao Zhang, Jiaming Zhang, Ziqi Zhang, Yinghui Wang · Feb 24, 2026 · Citations: 0
Automatic Metrics Long Horizon
Large Language Model (LLM) agents are susceptible to Indirect Prompt Injection (IPI) attacks, where malicious instructions in retrieved content hijack the agent's execution.
- A Systematic Review of Algorithmic Red Teaming Methodologies for Assurance and Security of AI Applications
Shruti Srivastava, Kiranmayee Janardhan, Shaurya Jauhari · Feb 24, 2026 · Citations: 0
Red Team Automatic Metrics
These limitations have driven the evolution toward auto-mated red teaming, which leverages artificial intelligence and automation to deliver efficient and adaptive security evaluations.
- Intent Laundering: AI Safety Datasets Are Not What They Seem
Shahriar Golchin, Marc Wetter · Feb 17, 2026 · Citations: 0
Red Team
We systematically evaluate the quality of widely used AI safety datasets from two perspectives: in isolation and in practice.
- Exposing the Systematic Vulnerability of Open-Weight Models to Prefill Attacks
Lukas Struppek, Adam Gleave, Kellin Pelrine · Feb 16, 2026 · Citations: 0
Red Team
We present the largest empirical study to date of prefill attacks, evaluating over 20 existing and novel strategies across multiple model families and state-of-the-art open-weight models.
- Jailbreaking Leaves a Trace: Understanding and Detecting Jailbreak Attacks from Internal Representations of Large Language Models
Sri Durga Sai Sowmya Kadali, Evangelos E. Papalexakis · Feb 12, 2026 · Citations: 0
Red Team
On an abliterated LLaMA-3.1-8B model, selectively bypassing high-susceptibility layers blocks 78% of jailbreak attempts while preserving benign behavior on 94% of benign prompts.
- From Threat Intelligence to Firewall Rules: Semantic Relations in Hybrid AI Agent and Expert System Architectures
Chiara Bonfanti, Davide Colaiacomo, Luca Cagliero, Cataldo Basile · Mar 4, 2026 · Citations: 0
- On the Suitability of LLM-Driven Agents for Dark Pattern Audits
Chen Sun, Yash Vekaria, Rishab Nithyanand · Mar 4, 2026 · Citations: 0
- Your Inference Request Will Become a Black Box: Confidential Inference for Cloud-based Large Language Models
Chung-ju Huang, Huiqiang Zhao, Yuanpeng He, Lijian Li, Wenpin Jiao · Feb 27, 2026 · Citations: 0
- Automated Vulnerability Detection in Source Code Using Deep Representation Learning
C. Seas, G. Fitzpatrick, J. A. Hamilton, M. C. Carlisle · Feb 26, 2026 · Citations: 0
- AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification
Tian Zhang, Yiwei Xu, Juan Wang, Keyan Guo, Xiaoyang Xu · Feb 26, 2026 · Citations: 0
- IMMACULATE: A Practical LLM Auditing Framework via Verifiable Computation
Yanpei Guo, Wenjie Qu, Linyu Wu, Shengfang Zhai, Lionel Z. Wang · Feb 26, 2026 · Citations: 0
- On the Effectiveness of Membership Inference in Targeted Data Extraction from Large Language Models
Ali Al Sahili, Ali Chehab, Razane Tajeddine · Dec 15, 2025 · Citations: 0
- LLM Novice Uplift on Dual-Use, In Silico Biology Tasks
Chen Bo Calvin Zhang, Christina Q. Knight, Nicholas Kruus, Jason Hausenloy, Pedro Medeiros · Feb 26, 2026 · Citations: 0
Large language models (LLMs) perform increasingly well on biology benchmarks, but it remains unclear whether they uplift novice users -- i.e., enable humans to perform better than with internet-only resources.
- A Decision-Theoretic Formalisation of Steganography With Applications to LLM Monitoring
Usman Anwar, Julianna Piskorz, David D. Baek, David Africa, Jim Weatherall · Feb 26, 2026 · Citations: 0
Our central insight is that steganography creates an asymmetry in usable information between agents who can and cannot decode the hidden content (present within a steganographic signal), and this otherwise latent asymmetry can be inferred…
- Assessing Deanonymization Risks with Stylometry-Assisted LLM Agent
Boyang Zhang, Yang Zhang · Feb 26, 2026 · Citations: 0
Automatic Metrics
In this work, we introduce an LLM agent designed to evaluate and mitigate such risks through a structured, interpretable pipeline.
- Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG
Inderjeet Singh, Vikas Pahuja, Aishvariya Priya Rathina Sabapathy, Chiara Picardi, Amit Giloni · Feb 24, 2026 · Citations: 0
Automatic Metrics
Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics across retrieval, planning, and generation components.
- SoK: Agentic Skills -- Beyond Tool Use in LLM Agents
Yanna Jiang, Delong Li, Haiyu Deng, Baihe Ma, Xu Wang · Feb 24, 2026 · Citations: 0
Tool Use
Agentic systems increasingly rely on reusable procedural capabilities, a.k.a., agentic skills, to execute long-horizon workflows reliably.
- AdapTools: Adaptive Tool-based Indirect Prompt Injection Attacks on Agentic LLMs
Che Wang, Jiaming Zhang, Ziqi Zhang, Zijie Wang, Yinghui Wang · Feb 24, 2026 · Citations: 0
Automatic Metrics
The integration of external data services (e.g., Model Context Protocol, MCP) has made large language model-based agents increasingly powerful for complex task execution.
- Personal Information Parroting in Language Models
Nishant Subramani, Kshitish Ghate, Mona Diab · Feb 24, 2026 · Citations: 0
Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.
- Watermarking LLM Agent Trajectories
Wenlong Meng, Chen Gong, Terry Yue Zhuo, Fan Zhang, Kecen Li · Feb 21, 2026 · Citations: 0
Long Horizon
LLM agents rely heavily on high-quality trajectory data to guide their problem-solving behaviors, yet producing such data requires substantial task design, high-capacity model generation, and manual filtering.
- TFL: Targeted Bit-Flip Attack on Large Language Model
Jingkai Guo, Chaitali Chakrabarti, Deliang Fan · Feb 19, 2026 · Citations: 0
Large language models (LLMs) are increasingly deployed in safety and security critical applications, raising concerns about their robustness to model parameter fault injection attacks.
- DAVE: A Policy-Enforcing LLM Spokesperson for Secure Multi-Document Data Sharing
René Brinkhege, Prahlad Menon · Feb 19, 2026 · Citations: 0
We therefore outline an evaluation methodology to assess security, utility, and performance trade-offs under benign and adversarial querying as a basis for future empirical work on systematically governed LLM access to multi-party data…
- A Content-Based Framework for Cybersecurity Refusal Decisions in Large Language Models
Noa Linder, Meirav Segal, Omer Antverg, Gil Gekker, Tomer Fichman · Feb 17, 2026 · Citations: 0
Large language models and LLM-based agents are increasingly used for cybersecurity tasks that are inherently dual-use.
- Weight space Detection of Backdoors in LoRA Adapters
David Puertolas Merenciano, Ekaterina Vasyagina, Raghav Dixit, Kevin Zhu, Ruizhe Li · Feb 16, 2026 · Citations: 0
Automatic Metrics
We evaluate the method on 500 LoRA adapters -- 400 clean, and 100 poisoned for Llama-3.2-3B on instruction and reasoning datasets: Alpaca, Dolly, GSM8K, ARC-Challenge, SQuADv2, NaturalQuestions, HumanEval, and GLUE dataset.
- Overthinking Loops in Agents: A Structural Risk via MCP Tools
Yohan Lee, Jisoo Jang, Seoyeon Choi, Sangyeop Kim, Seungtaek Choi · Feb 16, 2026 · Citations: 0
Tool-using LLM agents increasingly coordinate real workloads by selecting and chaining third-party tools based on text-visible metadata such as tool names, descriptions, and return messages.
- Differentially Private Retrieval-Augmented Generation
Tingting Tang, James Flemings, Yongqin Wang, Murali Annavaram · Feb 16, 2026 · Citations: 0
We evaluate DP-KSA on two QA benchmarks using three instruction-tuned LLMs, and our empirical results demonstrate that DP-KSA achieves a strong privacy-utility tradeoff.
- MCPShield: A Security Cognition Layer for Adaptive Trust Calibration in Model Context Protocol Agents
Zhenhong Zhou, Yuanhe Zhang, Hongwei Cai, Moayad Aloqaily, Ouns Bouachir · Feb 15, 2026 · Citations: 0
Tool Use
The Model Context Protocol (MCP) standardizes tool use for LLM-based agents and enable third-party servers.
- How Well Can LLM Agents Simulate End-User Security and Privacy Attitudes and Behaviors?
Yuxuan Li, Leyang Li, Hao-Ping Lee, Sauvik Das · Feb 6, 2026 · Citations: 0
Simulation Env
A growing body of research assumes that large language model (LLM) agents can serve as proxies for how people form attitudes toward and behave in response to security and privacy (S&P) threats.
