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.
Showing 61–80 of 927 papers
Clear filtersEzra Edelman, Benjamin Edelman, Surbhi Goel +1 more
Despite growing capabilities, LLMs remain susceptible to prompt injections: malicious inputs that can manipulate or hijack models. In particular, it...
Yubo Cui, Xianchao Guan, Zheng Zhang
Vision-language models (VLMs) exhibit strong zero-shot generalization but remain vulnerable to adversarial attacks. Existing adversarial fine-tuning...
Abdelkader Metales, Miriam Grabenheinrich
Continual learning methods for large language models increasingly decide what to learn by surprise, keeping the inputs the model finds least...
Hicham Hammouchi, Gabriele Lenzini
Large language model serving stacks, commercial inference APIs, vector databases, and agent orchestration gateways are now ex- posed on the public...
Zihou Ren, Byungchul Tak, Long Wang
Large language model (LLM) agents often receive access to more files, accounts, and services than a task requires. Prompt injection turns this excess...
Zhuoyun Li, Boxuan Wang, Jinwei Hu +2 more
Large Language Models (LLMs) are increasingly being used as cognitive interfaces that shape how users interpret evidence, communicate, and make...
Chengquan Guo, Xiaodong Liu, Weiwei Yang +7 more
LLM agents are increasingly adopted in long-horizon tasks and interact with diverse external environments. Despite their growing capabilities, these...
Long Dang, Ali Alfatemi, Kaiqi Xiong +4 more
Distributed machine learning (DML), including conventional distributed training and federated learning (FL), enables collaborative model development...
Pengfei Zeng, Changhua Luo, Penghui Li
Large language models (LLMs) have enabled more effective vulnerability scanning of web applications. However, these capabilities also make it...
Yuelin Han
Prior work framed the envelope layer as the channel through which malicious content enters an agent, and established it as a defense surface...
Abhijit Mazumdar, Manuela L. Bujorianu, Rafal Wisniewski
In this letter, we investigate a notion of probabilistic safety for a stochastic dynamical system. Standard safety notions are based on the idea that...
Vincess Dongmo, Astride Melvin Fokam Ninyim
Multi-relational graph detectors learn from clean data how much to trust each relation, but an adversary pays a different price to manipulate each...
Indraveni Chebolu, Seema Panghal, Arnab Mallick +1 more
Security pipelines often fuse several data sources and protect a downstream model with a cross-source anomaly detector. Such detectors are usually...
Yuchen Zhang, Yaxiong Wang, Yujiao Wu +6 more
Multimodal forgery detection is commonly studied under a static assumption: training attacks are expected to approximate those encountered after...
Saim Rehman, Muhammad Shafique
Compact electroencephalography (EEG) decoders enable embedded brain--computer interfaces (BCIs), but compression can alter adversarial robustness...
Casey M. Williams, Jennifer Victoria Scurrell
The gap between AI capability deployment and safety assurance is structural and cannot be closed through internal evaluations alone. Analyzing...
Sai Suresh Macharla Vasu, Sumedh Santosh Joshi, Philipp Christmann +6 more
The number of paper submissions to major AI conferences has increased dramatically in recent years, substantially increasing the peer-review workload...
Sheila Zingg, Florian Tramèr, Martin Vechev +1 more
There are different levels of transparency for LLMs: open weights, open data, and/or open pipeline. Recently, more interest has emerged in fully...
Yu Yan, Jiahao Chen, Siqi Lu +4 more
As large language models (LLMs) are increasingly integrated into Web search, online recommendation, and intelligent decision-making, the third-party...
Junfeng Jiao, Kevin Chen, Shrestha Mishra +4 more
AI systems are increasingly present in children's everyday environments: education, entertainment, social networks, and toys. Their safety is...
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 4,319+ 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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