AI Threat AlertSurveyMEDIUM
De-Anonymization at Scale via Tournament-Style Attribution
Lirui Zhang, Huishuai Zhang
As LLMs rapidly advance and enter real-world use, their privacy implications are increasingly important. We study an authorship de-anonymization...
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 101–120 of 140 papers
Clear filtersSurveyMEDIUM
Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale
Yi Liu, Weizhe Wang, Ruitao Feng +5 more
The rise of AI agent frameworks has introduced agent skills, modular packages containing instructions and executable code that dynamically extend...
SurveyMEDIUM
SoK: Privacy-aware LLM in Healthcare: Threat Model, Privacy Techniques, Challenges and Recommendations
Mohoshin Ara Tahera, Karamveer Singh Sidhu, Shuvalaxmi Dass +1 more
Large Language Models (LLMs) are increasingly adopted in healthcare to support clinical decision-making, summarize electronic health records (EHRs),...
SurveyMEDIUM
PenForge: On-the-Fly Expert Agent Construction for Automated Penetration Testing
Huihui Huang, Jieke Shi, Junkai Chen +6 more
Penetration testing is essential for identifying vulnerabilities in web applications before real adversaries can exploit them. Recent work has...
SurveyMEDIUM
CHASE: LLM Agents for Dissecting Malicious PyPI Packages
Takaaki Toda, Tatsuya Mori
Modern software package registries like PyPI have become critical infrastructure for software development, but are increasingly exploited by threat...
SurveyMEDIUM
STELP: Secure Transpilation and Execution of LLM-Generated Programs
Swapnil Shinde, Sahil Wadhwa, Andy Luo +2 more
Rapid evolution of Large Language Models (LLMs) has achieved major advances in reasoning, planning, and function-calling capabilities. Multi-agentic...
SurveyMEDIUM
Autonomous Agents on Blockchains: Standards, Execution Models, and Trust Boundaries
Saad Alqithami
Advances in large language models have enabled agentic AI systems that can reason, plan, and interact with external tools to execute multi-step...
SurveyMEDIUM
A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes
Sahaya Jestus Lazer, Kshitiz Aryal, Maanak Gupta +1 more
Agentic AI marks an important transition from single-step generative models to systems capable of reasoning, planning, acting, and adapting over...
SurveyMEDIUM
SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systems
Andreea-Elena Bodea, Stephen Meisenbacher, Alexandra Klymenko +1 more
The continued promise of Large Language Models (LLMs), particularly in their natural language understanding and generation capabilities, has driven a...
SurveyMEDIUM
Autonomous Threat Detection and Response in Cloud Security: A Comprehensive Survey of AI-Driven Strategies
Gaurav Sarraf, Vibhor Pal
Cloud computing has changed online communities in three dimensions, which are scalability, adaptability and reduced overhead. But there are serious...
SurveyMEDIUM
Automated Post-Incident Policy Gap Analysis via Threat-Informed Evidence Mapping using Large Language Models
Huan Lin Oh, Jay Yong Jun Jie, Mandy Lee Ling Siu +1 more
Cybersecurity post-incident reviews are essential for identifying control failures and improving organisational resilience, yet they remain...
SurveyMEDIUM
The Silicon Psyche: Anthropomorphic Vulnerabilities in Large Language Models
Giuseppe Canale, Kashyap Thimmaraju
Large Language Models (LLMs) are rapidly transitioning from conversational assistants to autonomous agents embedded in critical organizational...
SurveyMEDIUM
ChatGPT: Excellent Paper! Accept It. Editor: Imposter Found! Review Rejected
Kanchon Gharami, Sanjiv Kumar Sarkar, Yongxin Liu +1 more
Large Language Models (LLMs) like ChatGPT are now widely used in writing and reviewing scientific papers. While this trend accelerates publication...
SurveyMEDIUM
AprielGuard
Jaykumar Kasundra, Anjaneya Praharaj, Sourabh Surana +11 more
Safeguarding large language models (LLMs) against unsafe or adversarial behavior is critical as they are increasingly deployed in conversational and...
SurveyMEDIUM
Trust in LLM-controlled Robotics: a Survey of Security Threats, Defenses and Challenges
Xinyu Huang, Shyam Karthick V B, Taozhao Chen +5 more
The integration of Large Language Models (LLMs) into robotics has revolutionized their ability to interpret complex human commands and execute...
SurveyMEDIUM
Async Control: Stress-testing Asynchronous Control Measures for LLM Agents
Asa Cooper Stickland, Jan Michelfeit, Arathi Mani +6 more
LLM-based software engineering agents are increasingly used in real-world development tasks, often with access to sensitive data or security-critical...
SurveyMEDIUM
The Role of AI in Modern Penetration Testing
J. Alexander Curtis, Nasir U. Eisty
Penetration testing is a cornerstone of cybersecurity, traditionally driven by manual, time-intensive processes. As systems grow in complexity, there...
SurveyMEDIUM
Mapping AI Risk Mitigations: Evidence Scan and Preliminary AI Risk Mitigation Taxonomy
Alexander K. Saeri, Sophia Lloyd George, Jess Graham +4 more
Organizations and governments that develop, deploy, use, and govern AI must coordinate on effective risk mitigation. However, the landscape of AI...
SurveyMEDIUM
Systematization of Knowledge: Security and Safety in the Model Context Protocol Ecosystem
Shiva Gaire, Srijan Gyawali, Saroj Mishra +3 more
The Model Context Protocol (MCP) has emerged as the de facto standard for connecting Large Language Models (LLMs) to external data and tools,...
SurveyMEDIUM
SoK: Trust-Authorization Mismatch in LLM Agent Interactions
Guanquan Shi, Haohua Du, Zhiqiang Wang +4 more
Large Language Models (LLMs) are evolving into autonomous agents capable of executing complex workflows via standardized protocols (e.g., MCP)....
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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