AI Threat AlertToolMEDIUM
AgenTRIM: Tool Risk Mitigation for Agentic AI
Roy Betser, Shamik Bose, Amit Giloni +3 more
AI agents are autonomous systems that combine LLMs with external tools to solve complex tasks. While such tools extend capability, improper tool...
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 161–180 of 226 papers
Clear filtersToolMEDIUM
Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Framework
Zimo Ji, Daoyuan Wu, Wenyuan Jiang +5 more
Large Language Model (LLM)-based agent systems are increasingly deployed for complex real-world tasks but remain vulnerable to natural language-based...
ToolMEDIUM
Beyond Max Tokens: Stealthy Resource Amplification via Tool Calling Chains in LLM Agents
Kaiyu Zhou, Yongsen Zheng, Yicheng He +5 more
The agent--tool interaction loop is a critical attack surface for modern Large Language Model (LLM) agents. Existing denial-of-service (DoS) attacks...
ToolMEDIUM
SecMLOps: A Comprehensive Framework for Integrating Security Throughout the MLOps Lifecycle
Xinrui Zhang, Pincan Zhao, Jason Jaskolka +2 more
Machine Learning (ML) has emerged as a pivotal technology in the operation of large and complex systems, driving advancements in fields such as...
ToolMEDIUM
ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback
Yutao Mou, Zhangchi Xue, Lijun Li +4 more
While LLM-based agents can interact with environments via invoking external tools, their expanded capabilities also amplify security risks....
ToolMEDIUM
CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents
Hanna Foerster, Tom Blanchard, Kristina Nikolić +6 more
AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss....
ToolMEDIUM
Enhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Framework
Yixiao Peng, Hao Hu, Feiyang Li +5 more
While virtualization and resource pooling empower cloud networks with structural flexibility and elastic scalability, they inevitably expand the...
ToolMEDIUM
ResMAS: Resilience Optimization in LLM-based Multi-agent Systems
Zhilun Zhou, Zihan Liu, Jiahe Liu +5 more
Large Language Model-based Multi-Agent Systems (LLM-based MAS), where multiple LLM agents collaborate to solve complex tasks, have shown impressive...
ToolMEDIUM
Prompt-Counterfactual Explanations for Generative AI System Behavior
Sofie Goethals, Foster Provost, João Sedoc
As generative AI systems become integrated into real-world applications, organizations increasingly need to be able to understand and interpret their...
ToolMEDIUM
LAsset: An LLM-assisted Security Asset Identification Framework for System-on-Chip (SoC) Verification
Md Ajoad Hasan, Dipayan Saha, Khan Thamid Hasan +5 more
The growing complexity of modern system-on-chip (SoC) and IP designs is making security assurance difficult day by day. One of the fundamental steps...
ToolMEDIUM
Exposing Hidden Interfaces: LLM-Guided Type Inference for Reverse Engineering macOS Private Frameworks
Arina Kharlamova, Youcheng Sun, Ting Yu
Private macOS frameworks underpin critical services and daemons but remain undocumented and distributed only as stripped binaries, complicating...
ToolMEDIUM
Multi-Agent Framework for Threat Mitigation and Resilience in AI-Based Systems
Armstrong Foundjem, Lionel Nganyewou Tidjon, Leuson Da Silva +1 more
Machine learning (ML) underpins foundation models in finance, healthcare, and critical infrastructure, making them targets for data poisoning, model...
ToolMEDIUM
ReGAIN: Retrieval-Grounded AI Framework for Network Traffic Analysis
Shaghayegh Shajarian, Kennedy Marsh, James Benson +2 more
Modern networks generate vast, heterogeneous traffic that must be continuously analyzed for security and performance. Traditional network traffic...
ToolMEDIUM
Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection
Junjun Pan, Yixin Liu, Rui Miao +5 more
Large language model (LLM)-based multi-agent systems (MAS) have shown strong capabilities in solving complex tasks. As MAS become increasingly...
ToolMEDIUM
AI Code in the Wild: Measuring Security Risks and Ecosystem Shifts of AI-Generated Code in Modern Software
Bin Wang, Wenjie Yu, Yilu Zhong +6 more
Large language models (LLMs) for code generation are becoming integral to modern software development, but their real-world prevalence and security...
ToolMEDIUM
Verifiability-First Agents: Provable Observability and Lightweight Audit Agents for Controlling Autonomous LLM Systems
Abhivansh Gupta
As LLM-based agents grow more autonomous and multi-modal, ensuring they remain controllable, auditable, and faithful to deployer intent becomes...
ToolMEDIUM
Quantifying Return on Security Controls in LLM Systems
Richard Helder Moulton, Austin O'Brien, John D. Hastings
Although large language models (LLMs) are increasingly used in security-critical workflows, practitioners lack quantitative guidance on which...
ToolMEDIUM
Penetration Testing of Agentic AI: A Comparative Security Analysis Across Models and Frameworks
Viet K. Nguyen, Mohammad I. Husain
Agentic AI introduces security vulnerabilities that traditional LLM safeguards fail to address. Although recent work by Unit 42 at Palo Alto Networks...
ToolMEDIUM
MALCDF: A Distributed Multi-Agent LLM Framework for Real-Time Cyber
Arth Bhardwaj, Sia Godika, Yuvam Loonker
Traditional, centralized security tools often miss adaptive, multi-vector attacks. We present the Multi-Agent LLM Cyber Defense Framework (MALCDF), a...
ToolMEDIUM
From Obfuscated to Obvious: A Comprehensive JavaScript Deobfuscation Tool for Security Analysis
Dongchao Zhou, Lingyun Ying, Huajun Chai +1 more
JavaScript's widespread adoption has made it an attractive target for malicious attackers who employ sophisticated obfuscation techniques to conceal...
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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