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 21–40 of 101 papers
Clear filtersAndrei Alexandru Drăghici, Sediola Ruko, Briland Hitaj +1 more
Nowadays, large language models produce text at scale, and much of it is redistributed across platforms. A moderator who finds a suspicious comment,...
Seungdeok Roh, Seokjun Ryu, Sohee Park +1 more
LLM agents can exploit web vulnerabilities fast enough that patching may come too late. Deception against such agents has mostly lured them away from...
Xiaobo Chen, Genliang Zhu, Ziyuan Wang +1 more
Enterprise AI agents increasingly split a user's request across subagents and merge their results, and each handoff can summarize away, alter or...
Quentin Le Roux, Marti Jimenez, Yannick Teglia +1 more
The protection of mature, multi-stage Face Recognition Systems is crucial to system providers. This technical report targets the threat model most...
Yangguang Lin, Quan Fang, Xiaoyan Liu +1 more
Video Large Language Models (Video-LLMs) have achieved strong performance on many video understanding tasks, yet remain vulnerable to temporal...
Jiamin Chang, Kristina Nikolić, Rishi Sharma +7 more
Prompt injection is usually discussed and evaluated as a single, homogeneous type of attack. However, vulnerabilities reported in real-world systems...
Naohiro Fujie, Shigeya Suzuki
We evaluate, at implementation level and under an explicit threat model, the verification pipelines of three Verifiable Credential (VC) formats:...
Li Zheng, Simin Chen
Finite element method (FEM) solvers are foundational to scientific and engineering simulation, yet their susceptibility to \emph{efficiency attacks}...
Binhao Ma, Weikang Ding, Hanqing Guo +5 more
Speech watermarking is becoming a key defense for digital copyright and content authentication. Existing attacks treat evasion as a removal problem:...
Datta Manikanta Sri Hari Danduri, Connor Everett Glosner, Aravind Machiry
Securing bootloaders is critical to establishing the trusted computing base of modern systems. TianoCore's edk2, the de facto UEFI-based bootloader,...
Geyuan Wang, Shunyu Liu, Siyuan Liang +2 more
Large Language Models (LLMs) have shown promise for empowering search agents to tackle complex information-seeking tasks. However, since acquiring...
Xu Zhang, Zhichao Lu
As large language model (LLM) agents take on more complex tasks with greater autonomy, security has become a major obstacle to their broader...
Kainan Zhou, Gangzhen Qian, Janet Sung +3 more
An indirect prompt injection can affect a search-augmented large language model only after its carrier page reaches model context. SearchInject...
Junwei Yao, Jianan Liu, Yongxi Zhou +1 more
Programs that use a language model as a classifier act on its most probable label. Text injected into the input can move that label to one the...
Zewei Shi, Ruoxi Sun, Minhui Xue +4 more
Accurate electricity-demand forecasting is essential for the modern energy infrastructure. Recently, Times-Series Large Language Models (TS-LLMs)...
Subek Acharya, Syed Ayon, Khaleque Md Aashiq Kamal +3 more
Machine learning models have shown exceptional accuracy for ballot classification in election systems. Recent research has demonstrated that these...
Zhihua Wang, Yunhao Feng, Yifeng Gao +4 more
AI agents often rely on persistent memory to retain user preferences and operational rules across sessions. However, this capability introduces a new...
Mingchen Li, Ren-Yi Huang, Dumindu Samaraweera +2 more
Edge deployment of Large Language Models requires optimization techniques that reduce memory, computation, and energy costs. However, preserving task...
Ezra 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...
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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