AI Threat Alert indexes 3,397+ 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 761–780 of 1,668 papers
Clear filtersJia Hu, Youcheng Sun, Pierre Olivier
Software compartmentalization breaks down an application into compartments isolated from each other: an attacker taking over a compartment will be...
Ali Raza, Gurang Gupta, Nikolay Matyunin +1 more
Warning: This article includes red-teaming experiments, which contain examples of compromised LLM responses that may be offensive or upsetting. Large...
Amir Al-Maamari
Large Language Models (LLMs) show promise for Automated Program Repair (APR), yet their effectiveness on security vulnerabilities remains poorly...
Shaswata Mitra, Raj Patel, Sudip Mittal +2 more
Multi-agent systems (MAS) powered by LLMs promise adaptive, reasoning-driven enterprise workflows, yet granting agents autonomous control over tools,...
Harry Owiredu-Ashley
Most adversarial evaluations of large language model (LLM) safety assess single prompts and report binary pass/fail outcomes, which fails to capture...
Yinpeng Wu, Yitong Chen, Lixiang Wang +3 more
Device-side Large Language Models (LLMs) have witnessed explosive growth, offering higher privacy and availability compared to cloud-side LLMs....
Alexander Erlei, Lukas Meub
As AI agents increasingly act on behalf of human stakeholders in economic settings, understanding their behavior in complex market environments...
Bo Jiang
Knowledge distillation from proprietary LLM APIs poses a growing threat to model providers, yet defenses against this attack remain fragmented and...
Sumit Ranjan, Sugandha Sharma, Ubaid Abbas +1 more
Voice interfaces are quickly becoming a common way for people to interact with AI systems. This also brings new security risks, such as prompt...
Chenxi Li, Xianggan Liu, Dake Shen +9 more
Despite the rapid progress of Large Vision-Language Models (LVLMs), the integration of visual modalities introduces new safety vulnerabilities that...
Yuhang Huang, Boyang Ma, Biwei Yan +5 more
The Model Context Protocol (MCP) is an open and standardized interface that enables large language models (LLMs) to interact with external tools and...
Neha Nagaraja, Hayretdin Bahsi
Large Language Models (LLMs) are increasingly integrated into safety-critical workflows, yet existing security analyses remain fragmented and often...
Yige Li, Wei Zhao, Zhe Li +6 more
Backdoor mechanisms have traditionally been studied as security threats that compromise the integrity of machine learning models. However, the same...
Eduard Hirsch, Kristina Raab, Tobias J. Bauer +1 more
IT systems are facing an increasing number of security threats, including advanced persistent attacks and future quantum-computing vulnerabilities....
Yuxu Ge
Autonomous agents powered by large language models introduce a class of execution-layer vulnerabilities -- prompt injection, retrieval poisoning, and...
Punyajoy Saha, Sudipta Halder, Debjyoti Mondal +1 more
Safety alignment is critical for deploying large language models (LLMs) in real-world applications, yet most existing approaches rely on large...
Elzo Brito dos Santos Filho
AI-assisted software generation has increased development speed, but it has also amplified a persistent engineering problem: systems that are...
Donghwa Kang, Hojun Choe, Doohyun Kim +2 more
Deploying deep neural networks (DNNs) on edge devices exposes valuable intellectual property to model-stealing attacks. While TEE-shielded DNN...
Xisen Jin, Michael Duan, Qin Lin +4 more
As AI agents become widely deployed as online services, users often rely on an agent developer's claim about how safety is enforced, which introduces...
Jinman Wu, Yi Xie, Shen Lin +2 more
Safety alignment is often conceptualized as a monolithic process wherein harmfulness detection automatically triggers refusal. However, the...
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 3,397+ 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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