AI Threat Alert indexes 3,397+ 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 821–840 of 975 papers
Clear filtersPatrick Karlsen, Even Eilertsen
This paper investigates some of the risks introduced by "LLM poisoning," the intentional or unintentional introduction of malicious or biased data...
Hanzhong Liang, Yue Duan, Xing Su +5 more
As the Web3 ecosystem evolves toward a multi-chain architecture, cross-chain bridges have become critical infrastructure for enabling...
Siyuan Li, Yaowen Zheng, Hong Li +7 more
In modern software ecosystems, 1-day vulnerabilities pose significant security risks due to extensive code reuse. Identifying vulnerable functions in...
Ariyan Hossain, Khondokar Mohammad Ahanaf Hannan, Rakinul Haque +4 more
Gender bias in language models has gained increasing attention in the field of natural language processing. Encoder-based transformer models, which...
Heehwan Kim, Sungjune Park, Daeseon Choi
Large Language Models (LLMs) are generally equipped with guardrails to block the generation of harmful responses. However, existing defenses always...
Arnabh Borah, Md Tanvirul Alam, Nidhi Rastogi
Security applications are increasingly relying on large language models (LLMs) for cyber threat detection; however, their opaque reasoning often...
Zishuo Zheng, Vidhisha Balachandran, Chan Young Park +2 more
As large language model (LLM) based systems take on high-stakes roles in real-world decision-making, they must reconcile competing instructions from...
Aylton Almeida, Laerte Xavier, Marco Tulio Valente
Keeping software systems up to date is essential to avoid technical debt, security vulnerabilities, and the rigidity typical of legacy systems....
Shaked Zychlinski, Yuval Kainan
Large Language Models (LLMs) are susceptible to jailbreak attacks where malicious prompts are disguised using ciphers and character-level encodings...
Yingjia Wang, Ting Qiao, Xing Liu +3 more
The rapid advancement of deep neural networks (DNNs) heavily relies on large-scale, high-quality datasets. However, unauthorized commercial use of...
Zheng Zhang, Haonan Li, Xingyu Li +2 more
Bug bisection has been an important security task that aims to understand the range of software versions impacted by a bug, i.e., identifying the...
Kaiwen Zhou, Ahmed Elgohary, A S M Iftekhar +1 more
The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for...
André V. Duarte, Xuying li, Bin Zeng +3 more
If we cannot inspect the training data of a large language model (LLM), how can we ever know what it has seen? We believe the most compelling...
Emily Herron, Junqi Yin, Feiyi Wang
Large language models (LLMs) have demonstrated transformative potential in scientific research, yet their deployment in high-stakes contexts raises...
Simon Yu, Peilin Yu, Hongbo Zheng +3 more
We present VISAT, a novel open dataset and benchmarking suite for evaluating model robustness in the task of traffic sign recognition with the...
He Hu, Chiyuan Ma, Qianning Wang +5 more
The shortage of mental health professionals has driven the web to become a primary avenue for accessible psychological support. While Large Language...
Zheng Zhang, Guanlong Wu, Sen Deng +2 more
In the rapidly expanding landscape of Large Language Model (LLM) applications, real-time output streaming has become the dominant interaction...
Juan Ren, Mark Dras, Usman Naseem
Agentic methods have emerged as a powerful and autonomous paradigm that enhances reasoning, collaboration, and adaptive control, enabling systems to...
Yifan Wu, Xuewei Feng, Yuxiang Yang +1 more
As the core of the Internet infrastructure, the TCP/IP protocol stack undertakes the task of network data transmission. However, due to the...
María Sanz-Gómez, Víctor Mayoral-Vilches, Francesco Balassone +3 more
Cybersecurity spans multiple interconnected domains, complicating the development of meaningful, labor-relevant benchmarks. Existing benchmarks...
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 3,397+ 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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AI Threat Alert