AI Threat Alert indexes 3,023+ 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.
Showing 101–120 of 125 papers
Clear filtersCen Zhang, Younggi Park, Fabian Fleischer +20 more
DARPA's AI Cyber Challenge (AIxCC, 2023--2025) is the largest competition to date for building fully autonomous cyber reasoning systems (CRSs) that...
Yunlong Lyu, Yixuan Tang, Peng Chen +4 more
Modern AI-integrated IDEs are shifting from passive code completion to proactive Next Edit Suggestions (NES). Unlike traditional autocompletion, NES...
Yilin Geng, Omri Abend, Eduard Hovy +1 more
It is not only what we ask large language models (LLMs) to do that matters, but also how we prompt. Phrases like "This is urgent" or "As your...
Luze Sun, Alina Oprea, Eric Wong
LLM-based vulnerability detectors are increasingly deployed in security-critical code review, yet their resilience to evasion under...
Pedro H. Barcha Correia, Ryan W. Achjian, Diego E. G. Caetano de Oliveira +5 more
The rapid advancement and widespread adoption of generative artificial intelligence (GenAI) and large language models (LLMs) has been accompanied by...
Mohsen Hatami, Van Tuan Pham, Hozefa Lakadawala +1 more
The increasing integration of AI agents into cyber-physical systems (CPS) introduces new security risks that extend beyond traditional cyber or...
Wachiraphan Charoenwet, Kla Tantithamthavorn, Patanamon Thongtanunam +3 more
Secure code review is critical at the pre-commit stage, where vulnerabilities must be caught early under tight latency and limited-context...
Hugo Silva, Mateus Mendes, Hugo Gonçalo Oliveira
Large language models (LLMs) are evolving fast and are now frequently used as evaluators, in a process typically referred to as LLM-as-a-Judge, which...
Xiaowei Fu, Lei Zhang
The widespread use of Vision Language Models (VLMs, e.g. CLIP) has raised concerns about their vulnerability to sophisticated and imperceptible...
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...
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...
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),...
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...
Masahiro Kaneko
The use of large language models (LLMs) in peer review systems has attracted growing attention, making it essential to examine their potential...
Takaaki Toda, Tatsuya Mori
Modern software package registries like PyPI have become critical infrastructure for software development, but are increasingly exploited by threat...
Abdulhadi Shoufan, Ahmad-Azmi-Abdelhamid Esmaeil
As students increasingly rely on large language models, hallucinations pose a growing threat to learning. To mitigate this, AI literacy must expand...
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...
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...
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...
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...
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.
AI Threat Alert indexes 3,023+ papers on AI/ML security, classified across attack, defense, benchmark, survey, and tool categories and updated continuously.
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.
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.
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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