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 201–220 of 388 papers
Clear filtersPatrick Cooper, Alireza Nadali, Ashutosh Trivedi +1 more
Large language models (LLMs) are known to exhibit brittle behavior under adversarial prompts and jailbreak attacks, even after extensive alignment...
Ching-Yun Ko, Pin-Yu Chen
Modern artificial intelligence (AI) models are deployed on inference engines to optimize runtime efficiency and resource allocation, particularly for...
Poushali Sengupta, Shashi Raj Pandey, Sabita Maharjan +1 more
Large language models (LLMs) generate outputs by utilizing extensive context, which often includes redundant information from prompts, retrieved...
Eliron Rahimi, Elad Hirshel, Rom Himelstein +3 more
Diffusion language models (DLMs) have recently emerged as a promising alternative to autoregressive (AR) models, offering parallel decoding and...
Xinyi Hou, Shenao Wang, Yifan Zhang +4 more
Agentic AI systems built around large language models (LLMs) are moving away from closed, single-model frameworks and toward open ecosystems that...
Manveer Singh Tamber, Hosna Oyarhoseini, Jimmy Lin
Research on adversarial robustness in language models is currently fragmented across applications and attacks, obscuring shared vulnerabilities. In...
Haitham S. Al-Sinani, Chris J. Mitchell
Wireless ethical hacking relies heavily on skilled practitioners manually interpreting reconnaissance results and executing complex, time-sensitive...
Mingqian Feng, Xiaodong Liu, Weiwei Yang +3 more
Large Language Models (LLMs) are typically evaluated for safety under single-shot or low-budget adversarial prompting, which underestimates...
Amirhossein Taherpour, Xiaodong Wang
Federated learning (FL) enables collaborative model training while preserving data privacy, yet both centralized and decentralized approaches face...
Mingyang Liao, Yichen Wan, shuchen wu +6 more
LLM-based role-playing has rapidly improved in fidelity, yet stronger adherence to persona constraints commonly increases vulnerability to jailbreak...
Wenhui Zhang, Huiyu Xu, Zhibo Wang +4 more
Recent advancements in multi-model AI systems have leveraged LLM routers to reduce computational cost while maintaining response quality by assigning...
Alvi Md Ishmam, Najibul Haque Sarker, Zaber Ibn Abdul Hakim +1 more
Multimodal Large Language Models (MLLMs) have achieved remarkable performance across vision-language tasks. Recent advancements allow these models to...
Arther Tian, Alex Ding, Frank Chen +2 more
Decentralized large language model inference networks require lightweight mechanisms to reward high quality outputs under heterogeneous latency and...
Jarrod Barnes
As large language models (LLMs) improve, so do their offensive applications: frontier agents now generate working exploits for under $50 in compute...
Onkar Shelar, Travis Desell
Evolutionary prompt search is a practical black-box approach for red teaming large language models (LLMs), but existing methods often collapse onto a...
Yizhong Ding
Webshells remain a primary foothold for attackers to compromise servers, particularly within PHP ecosystems. However, existing detection mechanisms...
Yangyang Guo, Ziwei Xu, Si Liu +2 more
This study reveals a previously unexplored vulnerability in the safety alignment of Large Language Models (LLMs). Existing aligned LLMs predominantly...
Sen Nie, Jie Zhang, Zhuo Wang +2 more
Vision-language models (VLMs) such as CLIP have demonstrated remarkable zero-shot generalization, yet remain highly vulnerable to adversarial...
Jiankai Jin, Xiangzheng Zhang, Zhao Liu +2 more
Machine learning systems can produce personalized outputs that allow an adversary to infer sensitive input attributes at inference time. We introduce...
Andy Zhu, Rongzhe Wei, Yupu Gu +1 more
Machine unlearning (MU) for large language models has become critical for AI safety, yet existing methods fail to generalize to Mixture-of-Experts...
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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