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 301–320 of 1,741 papers
Clear filtersChengtao Jian, Kai Yang
Direct Preference Optimization (DPO) has become a widely adopted approach for aligning vision-language models (VLMs), yet the security of multimodal...
Nutan Kumar Naik, Vijay Prasad Poudel, Aditya Kumar Saroj +2 more
Benchmarks report what an Artificial Intelligence (AI) agent can do on a task suite, but not whether one released version has been engineered with...
Tobias Hürten, Johannes F. Loevenich, Florian Spelter +5 more
This paper proposes an Autonomous Cyber Operations (ACO) gym, a unified framework integrating high-fidelity network emulation, realistic user...
Inderjeet Singh
Finite safety assessments cannot exclude an adversary that preserves unattacked behavior until certification and activates afterward. We characterize...
Dominik Seip, Bartol Markovinović, Matthias Hein
The safety of LLM-based agents currently rests on alignment training and safety classifiers, neither of which can rule out that a sophisticated...
Kristoffer Hempel, Nils Loose, Lorenzo Cavallaro +1 more
Large language model (LLM) agents are becoming an integral part of the software development lifecycle, and the capabilities granted to them allow an...
Xi Chen, Chunyi Zhou, Rui Zeng +3 more
Federated fine-tuning of large language models (LLMs) allows multiple clients to collaboratively adapt models without sharing raw data, offering...
Kaan Kale, Sriram Vishwanath
Linear probes on a model's hidden states are used as cheap monitors for indirect prompt injection: they screen the tool outputs an agent reads and...
Paweł Batorski, Przemysław Spurek, Paul Swoboda
Multi-bit watermarking embeds a message, typically an account identifier, into LLM output so that harmful text can be traced to its origin. We show...
Yan-Lun Chen, Pin-Yu Chen, Chia-Mu Yu
Third-party skills can expose LLM agents to malicious instructions hidden by encoding. We introduce Ciphered Skill Injection (CSI), which embeds such...
Qikun Cai, Tao Huang, Chen Hou
LLM agents now act as delegated communication intermediaries: they read private context, draft messages, and send externally visible emails on behalf...
Wenbin Chen, Jiexin Wang, Guanting Wu +2 more
The code generation capabilities of large language models (LLMs) can equally be misused to implement harmful functionality, underscoring the need to...
Juann Kim, Minku Kang
Persona vectors are linear directions in the residual-stream activations of LLMs that encode behavioral traits and enable fine-grained control over...
Fasheng Miao, Zhaoyu Hu, Xiang Li +5 more
Large language model (LLM) agents are increasingly used for vulnerability analysis, yet existing benchmarks provide limited coverage of exploitation...
Xiang Li, Aichun Huang, Mingming Zhang +1 more
TypeSafe AI's Jev is a new kind of safety judge. It generates no text and no label: it answers questions the developer defines in advance and returns...
Jerrold Huang, Junwei Quan, Saiyue Lyu +2 more
Agent skills turn natural-language instructions into actions, creating a security challenge: legitimate automation and malicious behaviour can look...
Amman Yusuf, Jiwoo Shin, Ruizhi Deng +3 more
The Safety-Aware Denoiser (SAD) is a recent, training-free, safety-guidance framework for text diffusion models. During inference, SAD steers text...
Maria-Ioana-Andrea Ivan, Orhan Ermis, Ruxandra F. Olimid
With recent developments in Artificial Intelligence (AI), the adoption of distributed learning paradigms such as Federated Learning (FL) has emerged....
Yuetian Chen, Kaiyuan Zhang, Yuntao Du +6 more
Browser agents need to make decisions and execute actions on behalf of users while consuming untrusted webpages, creating opportunities for malicious...
Ali Javeri, Javad Hezareh, Radin Shahdaei +1 more
Score-based black-box adversarial attacks pose a serious threat to deployed machine learning systems by exploiting repeated model queries to craft...
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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