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 261–280 of 888 papers
Clear filtersManali Dangarikar, Cory Merkel
Deep neural networks (DNNs) deployed on resource-constrained neuromorphic hardware face three concurrent challenges: the need for model compression...
Narendra Kumar Dewangan, Mounira Msahli
Vehicle-to-everything (V2X) systems increasingly incorporate large language models (LLMs) for semantic tasks such as message summarization, operator...
Zhijing Hu, Changjun Fan, Yufan Deng +1 more
Network dismantling is fundamental to analyzing the robustness and vulnerability of complex systems, yet practical heuristics must balance...
Nizhang Li, Zonghao Ying, Xiangfan Wu +7 more
External skills extend the capabilities of large language model agents, but also introduce an execution-time attack surface: a skill that appears...
Yiming Chen, Kemou Li, Haiwei Wu +1 more
Backdoor attacks in multimodal contrastive learning (MCL) have garnered growing attention in recent years, as many downstream tasks critically depend...
Francis Heylighen
AI systems based on Large Language Models (LLMs) have prompted fears that they may harbor hidden goals, seek to dominate or eliminate humanity, or...
Jian Zhao, Shenao Wang, Qingyang Wu +3 more
The widespread adoption of open source software (OSS) has introduced significant security risks, with malicious code poisoning attacks increasingly...
Haocheng Fu, Yuqi Qian, Luyao Wang +1 more
Large language model (LLM) watermarking provides an important mechanism for tracing the provenance of generated text. Existing statistical watermarks...
Walid Saidi
Persistent agent memory must adapt as later outcomes change earlier evidence, yet mutable retrieval weights create an attribution problem: reviewers...
Giorgio Severi, Shujaat Mirza, Blake Bullwinkel +1 more
Chain-of-thought (CoT) monitoring is an increasingly important component of AI safety stacks but relies on the assumption that a model's reasoning...
Yiran Gao, Tao Li, Kim Hammar
Incident response is currently managed by security operators using predefined playbooks, resulting in slow, labor-intensive security decision-making...
Vincenzo Longo, Alberto Verna, Nikhil Jha +1 more
The Microsoft 365 (M365) ecosystem hosts thousands of third-party applications that integrate with enterprise tenants via fine-grained OAuth...
Giovanni Pizzenti, Alberto Verna, Nikhil Jha +3 more
In recent years, cybersecurity threats have increasingly exploited human behaviour rather than purely technical vulnerabilities, exposing the limits...
Junyeong Park, Jieun Han, Haneul Yoo +3 more
Large language models (LLMs) are increasingly used across diverse tasks in K-12 education, yet existing safety evaluations rarely examine how harmful...
Jia-Chen Zhang, Ze-Yu Zhang, Kai-Wei Zhang
Computer-use agents (CUAs), which empower large language models to autonomously operate operating systems and the web, are increasingly vulnerable to...
Yuekun Wang, Mingfei Cheng, Xiaofei Xie
LLM-based code auditors are increasingly integrated into pull-request (PR) workflows, yet their reliability against adversarial changes distributed...
Qianlong Yang, Bowen Ye, Xianda Guo +4 more
Despite the progress of multimodal large language models (MLLMs), they continue to exhibit deficiencies in visual perception. Following visual...
Alireza Lotfi, Subangkar Karmaker Shanto, Imtiaz Karim +1 more
Autonomous agents are increasingly used to execute consequential tasks in environments governed by operational constraints, organizational policies,...
Zheng Wu, Chenhao Xue, Shijie Zheng +3 more
As large language models (LLMs) continue to advance in complex reasoning tasks, they have learned to heavily prioritize explicit conditions provided...
Fazhong Liu, Zhuoyan Chen, Haozhen Tan +3 more
World models give embodied AI a predictive core: they compress observations into states, simulate action-conditioned futures, and enable planning...
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.
Get breaking CVE alerts, compliance reports (ISO 42001, EU AI Act), and CISO risk assessments for your AI/ML stack.
Start 14-Day Free Trial
AI Threat Alert