AI Threat Alert indexes 3,771+ 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 41–60 of 3,771 papers
Vansh Wahi
A higher evaluation score does not always mean a better language model system. When optimization exploits an evaluator's mistakes, measured progress...
Dahlia Shehata, Ming Li
Large language models (LLMs) exhibit a parametric vulnerability to adversarial swarm consensus. To mitigate this sycophancy, we introduce Contrastive...
Volkan Dağlı, Zerrin Dağlı, Dağhan Dağlı
Deploying Large Language Models for runtime operational triage incurs prohibitive latency (>100-500 ms), high VRAM requirements (>4-8 GB), and...
Abdullahil Kafi, Alvi Ataur Khalil
Retrieval augmented generation (RAG) systems have emerged as the dominant architecture for grounding large language model (LLM) outputs in verifiable...
Jianzhe Lin, Xiaolin Li, Fei Wang +3 more
Dialogue failures in language models are usually framed as memory failures: context too long, summaries lossy, a constraint forgotten. We argue this...
Ariel Flint, Luca Maria Aiello, Sara M. Constantino +2 more
As generative AI agents are deployed at scale, safety will depend not only on technical safeguards and individual model design, but also on...
Fernando Outeda, Gustavo Betarte, Juan Diego Campo +1 more
Large language models (LLMs) are increasingly deployed in production systems, raising concerns about their exposure to adversarial manipulation...
Jiling Zhou, Aisvarya Adeseye, Antti Hakkala +2 more
Large Language Models (LLMs) are increasingly used in cybersecurity, where accurate analysis often requires multi-step and context-dependent...
Chetan Pathade, Prathamesh Pawar, Shubham Patil
Attack success rate (ASR) is the headline metric in nearly every published evaluation of attacks on, and defenses for, LLM agents. We argue that ASR...
Wai Kin Wong, Dongwei Xiao, Anthony Cheuk Tung Lai +2 more
The security of the modern web depends on the correctness of JavaScript (JS) engines, yet these complex systems remain vulnerable to high-impact...
Kaiyuan Zhang, Yuke Peng, Ke Jiang +1 more
Large language model (LLM) agents increasingly execute long-horizon workflows through external tools, allowing untrusted outputs to influence...
Kaiyuan Zhang, Yuke Peng, Ke Jiang +1 more
Large language model (LLM) agents increasingly execute long-horizon workflows through external tools, allowing untrusted outputs to influence...
Florian Krone, Elena Hoemann, Sven Hallerbach
Neural networks, both convolution or transformer based, are essential for modern computer vision systems. However, they are vulnerable to small...
Akihisha Fujiyama, Niwase Shamim
AI is increasingly used to automate computer security testing, and the tools must decide for themselves whether an attack succeeded. A finding that a...
Heewon Baek, Alsharif Abuadbba, Kristen Moore +2 more
Agentic AI extends LLM security beyond generated content to persistent state, autonomous actions, tool use, and interactions with humans and other...
Rohit Patel, Susil Kumar Mohanty, Jeenal Chaudhary
Root cause analysis at a remote site is slow: evidence is scattered across pod logs, Kubernetes events and cluster-level objects, and many operators...
Ben Shapira, Roi Cohen, Shang-Tse Chen +1 more
With face-recognition models now embedded in everyday authentication and surveillance, recent works have pinpointed a critical weakness: these models...
Frank E. Bobe, Gregory D. Vetaw, Darshan W. Bryner +2 more
Activation steering modifies LLM behavior at inference time, but identifying where and how strongly to steer remains manual. We introduce Deep Noir,...
Chao Huang, Meng Tong, Kejiang Chen
While multimodal large language models (MLLMs) enable a wide range of image-text reasoning tasks, recent incidents indicate that they are vulnerable...
Leilei Chen, Lan Zhang, Chen Tang +5 more
In pay-per-token LLM services, the more a model says, the more users pay. Dishonest providers can covertly manipulate generation to inflate output...
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 3,771+ 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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