AI Threat Alert indexes 3,082+ 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 1661–1680 of 3,082 papers
Kunal Mukherjee, Zulfikar Alom, Tran Gia Bao Ngo +2 more
The rise of bot accounts on social media poses significant risks to public discourse. To address this threat, modern bot detectors increasingly rely...
Luze Sun, Alina Oprea, Eric Wong
LLM-based vulnerability detectors are increasingly deployed in security-critical code review, yet their resilience to evasion under...
Abhishek Mishra, Mugilan Arulvanan, Reshma Ashok +3 more
Emergent misalignment poses risks to AI safety as language models are increasingly used for autonomous tasks. In this paper, we present a population...
Rory Driscoll, Alexandros Christoforos, Chadbourne Davis
While sequential reasoning enhances the capability of Vision-Language Models (VLMs) to execute complex multimodal tasks, their reliability in...
Ye Yu, Haibo Jin, Yaoning Yu +2 more
Large audio-language models increasingly operate on raw speech inputs, enabling more seamless integration across domains such as voice assistants,...
Saeid Jamshidi, Omar Abdul Wahab, Foutse Khomh +1 more
Federated learning (FL) has become an effective paradigm for privacy-preserving, distributed Intrusion Detection Systems (IDS) in cyber-physical and...
Edward Y. Chang, Longling Geng
Inference-time scaling can amplify reasoning pathologies: sycophancy, rung collapse, and premature certainty. We present RAudit, a diagnostic...
Saeid Jamshidi, Kawser Wazed Nafi, Arghavan Moradi Dakhel +3 more
Large Language Models (LLMs) are increasingly adopted in sensitive domains such as healthcare and financial institutions' data analytics; however,...
Haitham S. Al-Sinani, Chris J. Mitchell
Wireless ethical hacking relies heavily on skilled practitioners manually interpreting reconnaissance results and executing complex, time-sensitive...
Zhixiang Zhang, Zesen Liu, Yuchong Xie +2 more
Semantic caching has emerged as a pivotal technique for scaling LLM applications, widely adopted by major providers including AWS and Microsoft. By...
Yanghao Su, Wenbo Zhou, Tianwei Zhang +4 more
Emergent Misalignment refers to a failure mode in which fine-tuning large language models (LLMs) on narrowly scoped data induces broadly misaligned...
Wei Chen, Zhiyuan Peng, Xin Yin +4 more
Smart contracts are the backbone of the decentralized web, yet ensuring their functional correctness and security remains a critical challenge. While...
Yunpeng Xiong, Ting Zhang
Static Application Security Testing (SAST) tools are essential for identifying software vulnerabilities, but they often produce a high volume of...
Evgeny Grigorenko, David Stanojević, David Ilić +2 more
Modern Integrated Development Environments (IDEs) increasingly leverage Large Language Models (LLMs) to provide advanced features like code...
Farnaz Soltaniani, Shoaib Razzaq, Mohammad Ghafari
Early detection of security bug reports (SBRs) is critical for timely vulnerability mitigation. We present an evaluation of prompt-based engineering...
Waleed Khan Mohammed, Zahirul Arief Irfan Bin Shahrul Anuar, Mousa Sufian Mousa Mitani +2 more
Advanced Persistent Threats (APTs) are among the most challenging cyberattacks to detect. They are carried out by highly skilled attackers who...
Eduardo C. Garrido-Merchán, Adriana Constanza Cirera Tirschtigel
As Large Language Models become ubiquitous sources of health information, understanding their capacity to accurately represent stigmatized conditions...
Charles Westphal, Keivan Navaie, Fernando E. Rosas
Fine-tuned LLMs can covertly encode prompt secrets into outputs via steganographic channels. Prior work demonstrated this threat but relied on...
Haoyun Yang, Ronghong Huang, Yong Fang +4 more
Transport Layer Security (TLS) is fundamental to secure online communication, yet vulnerabilities in certificate validation that enable...
Yilong Huang, Songze Li
Diffusion-based face swapping achieves state-of-the-art performance, yet it also exacerbates the potential harm of malicious face swapping to violate...
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 3,082+ 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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