AI Threat AlertAttackMEDIUM
HoneyRoute: Honeypot-Model Routing for Adversarial LLM Serving
Han Jin
We introduce HoneyRoute, an inference-serving layer that detects whether an incoming request is malicious and, if so, routes it to a dedicated...
AI Security Research
AI Threat Alert indexes 4,319+ peer-reviewed and preprint papers on AI/ML security — covering adversarial attacks, model defenses, red-teaming benchmarks, surveys, and security tooling. Papers are sourced from arXiv, classified by type and by relevance to real-world threats, and cross-referenced with the CVEs and incidents they relate to.
- Adversarial attacks
- Model defenses
- Red-teaming benchmarks
- Surveys
- Security tooling
- Total4,319
- Attack1,676
- Benchmark1,229
- Defense610
- Tool432
- Survey252
Showing 361–380 of 2,152 papers
Clear filtersDefenseMEDIUM
Style Over Substance: Content-Invariant Wrappers Flip LLM Safety-Judge Verdicts
Yongxi Zhou, Wenbo Ye, Yuanzhe Liu +2 more
Automatic safety judges -- systems such as Llama Guard or a GPT-4o grading prompt that decide whether a model's reply is harmful -- produce the...
AttackMEDIUM
LLM-Based Penetration Testing in the Presence of Honeypots
Xinhong Xie, Piyush Nagasubramaniam, Neeraj Karamchandani +1 more
Large language model (LLM) agents are increasingly employed for offensive cybersecurity tasks such as automated vulnerability discovery,...
OtherMEDIUM
Popular Knowledge Propagates More Errors in LLM Knowledge Updating
Yuji Zhang, Weibing Wang, Cheng Qian +5 more
Updating a language model's knowledge through fine-tuning is essential for keeping its outputs current, yet can also induce factual forgetting and...
BenchmarkMEDIUM
"Shut Up and Let Me Enjoy My Otome": Understanding and Measuring the Toxicity in Otome Game Communities
Yage Zhang, Xinyue Shen, Yukun Jiang +2 more
Otome games, a romance simulation genre primarily targeting female, have emerged as a major force in the global gaming market, attracting hundreds of...
AttackMEDIUM
Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration
Xiao Ma, Hong Shen, Hui Tian +2 more
This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine...
AttackMEDIUM
Towards a Resilience-Theoretic Foundation for Adversarial Robustness in Industrial Control System Anomaly Detection
Branka Stojanović, Andreas Flatscher, Michael Somma
Anomaly-based intrusion detection systems in industrial control systems (ICS) and operational technology (OT) environments are increasingly required...
BenchmarkMEDIUM
SENTINEL-RL: Offloading Topological Reasoning from LLM Agents in the Security Operations Center
Uday Vallabhaneni, Cassie L. Cagwin, David J. Wild
Large language model (LLM) agents are increasingly proposed as autonomous SOC analysts, but two limitations make them unreliable at enterprise scale:...
DefenseMEDIUM
Representational alignment yields generalizable safety in language models
Lingyu Li, Yan Teng, Yingchun Wang +1 more
Aligning large language models (LLMs) is essential for their safe deployment. Current alignment methods mainly optimize observable responses, yet...
BenchmarkMEDIUM
Inferring Hidden User Models from the Behavior of Personalized LLM Agents
Haoyang Li, Yaxin Xiao, Qingqing Ye +2 more
Recent personalized LLM agents increasingly transform information retained in memory into compressed or structured representations, which we call...
BenchmarkMEDIUM
Remember and Reweight: Enhancing Multi-Agent Debate with Experience Memory and Confidence Estimation
Xuanfa Jin, Zhijian Ma, Yongcheng Zeng +3 more
Multi-agent debate (MAD) improves the reasoning capabilities of large language models by having multiple agents iteratively refine their responses...
DefenseMEDIUM
Privacy, Robustness, and Fairness Trade-offs in Federated Intrusion Detection: Geometric Indistinguishability at the Aggregation Interface
Adrita Rahman Tory, ABM Shawkat Ali, Md Abu Layek +1 more
Federated learning enables privacy-conscious collaboration for network intrusion detection without centralizing sensitive traffic data, yet its...
AttackMEDIUM
Trust Me, I'm Your Developer: Self-Issued Authentication in Large Language Models
Syed Ghazanfar Abbas, Dongyan Xu
Large language model (LLM) security has largely focused on role-playing jailbreaks, with less attention to what happens when a user asks an LLM to...
BenchmarkMEDIUM
Compound Prompt Constraints in LLM Code Generation: A Factorial Study of Format, Persona, and Urgency
Shrenik Jadhav, Nickalsa LaPlaca, Caleb Stone +3 more
Large language models (LLMs) are increasingly used in software engineering pipelines for code generation, where production prompts often combine...
DefenseMEDIUM
Beyond Small Patches: Black-Box Detection and Purification of Diverse Backdoor Triggers
Ahmed Abdelnaby, Mohamed Elmahallawy
Deep neural networks (DNNs) are increasingly deployed in real-world vision systems, yet their predictions can be covertly manipulated by backdoor...
ToolMEDIUM
Differentially private federated learning with Byzantine-robust aggregation: A cross-domain framework for secure model training in banking and healthcare systems
Srikumar Nayak
Federated learning allows banks, hospitals, and other regulated organizations to train a shared model without moving raw records off their own...
ToolMEDIUM
ACLE-MCP: Attested Capability Leases for Execution-Time Trust in Remote LLM Tool Use
Zhiyang Ding, Yang Luo, Guangpu Chen +2 more
Remote Model Context Protocol (MCP) services enable large language model agents to invoke external tools, but OAuth authorization alone does not...
AttackMEDIUM
A Finger on the Scale: Covert Policy Steering through Agentic Skills
Jiarui Li, Jiahao Chen, Chunyi Zhou +5 more
Reusable agent skills extend large language model (LLM) agents with task procedures, tool-use guidance, and output constraints. Yet these skills also...
OtherMEDIUM
Scalable Kronecker-Fisher Approximation: Efficient Hessian Analysis for Billion-Parameter Language Models Compression
Viacheslav Yusupov, Daria Cherniuk, Evgeny Frolov
In this paper, we propose a scalable Kronecker-based approximation that captures cross-layer interactions without storing the entire Fisher matrix,...
DefenseMEDIUM
Privacy Leakage in Federated Learning: Gradient-Based Client Identity Inference and Defenses for Inertial Sensing in Vehicular Edge Networks
Ali Akarma, Toqeer Ali Syed, Muhammad Khan +2 more
As vehicular networks move toward 5G/6G edge intelligence, federated learning (FL) is widely promoted as a privacy-preserving way for vehicles and...
Frequently asked questions
What is AI security research?
AI security research studies how AI and machine-learning systems can be attacked and defended — covering adversarial examples, prompt injection, model poisoning, training-data extraction, and the mitigations against them. AI Threat Alert curates this research from academic sources so security teams can track the threats behind emerging AI risks.
How many AI security papers does AI Threat Alert track?
AI Threat Alert indexes 4,319+ papers on AI/ML security, classified across attack, defense, benchmark, survey, and tool categories and updated continuously.
Where do the research papers come from?
Papers are sourced from arXiv, then classified by type and by relevance to real-world AI/ML threats, and cross-referenced with the CVEs and incidents they relate to.
What topics does the AI security research cover?
Coverage spans adversarial attacks, model and system defenses, red-teaming benchmarks, literature surveys, and security tooling for LLMs, ML libraries, AI agents, and inference pipelines.
How is this different from a generic paper search?
Every paper is filtered for AI security relevance and linked to the vulnerabilities, vendors, and incidents it relates to, so the research connects directly to operational threat intelligence.
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