AI Threat Alert indexes 3,371+ 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 81–100 of 130 papers
Clear filtersYow-Fu Liou, Yu-Chien Tang, Yu-Hsiang Liu +1 more
Benchmarking large language models (LLMs) is critical for understanding their capabilities, limitations, and robustness. In addition to interface...
Chutian Huang, Dake Cao, Jiacheng Ji +3 more
Background: While Large Language Models (LLMs) have achieved widespread adoption, malicious prompt engineering specifically "jailbreak attacks" poses...
Haoze Guo, Ziqi Wei
Retrieval-augmented generation (RAG) systems put more and more emphasis on grounding their responses in user-generated content found on the Web,...
Shaznin Sultana, Sadia Afreen, Nasir U. Eisty
Context: Traditional software security analysis methods struggle to keep pace with the scale and complexity of modern codebases, requiring...
Quy-Anh Dang, Chris Ngo, Truong-Son Hy
As large language models (LLMs) become integral to safety-critical applications, ensuring their robustness against adversarial prompts is paramount....
Zejian Chen, Chaozhuo Li, Chao Li +3 more
This paper provides a systematic survey of jailbreak attacks and defenses on Large Language Models (LLMs) and Vision-Language Models (VLMs),...
Xiangzhe Yuan, Zhenhao Zhang, Haoming Tang +1 more
As LLMs gain persuasive agentic capabilities through extended dialogues, they introduce novel risks in multi-turn conversational scams that...
Songyang Liu, Chaozhuo Li, Rui Pu +5 more
Jailbreak attacks present a significant challenge to the safety of Large Language Models (LLMs), yet current automated evaluation methods largely...
Md Hasan Saju, Maher Muhtadi, Akramul Azim
The rapid advancement of Large Language Models (LLMs) presents new opportunities for automated software vulnerability detection, a crucial task in...
Jingyu Zhang
Customer-service LLM agents increasingly make policy-bound decisions (refunds, rebooking, billing disputes), but the same ``helpful'' interaction...
Manu, Yi Guo, Kanchana Thilakarathna +5 more
Large Language Models (LLMs) can be driven into over-generation, emitting thousands of tokens before producing an end-of-sequence (EOS) token. This...
Woorim Han, Yeongjun Kwak, Miseon Yu +4 more
Learning-based automated vulnerability repair (AVR) techniques that utilize fine-tuned language models have shown promise in generating vulnerability...
Chinmay Pushkar, Sanchit Kabra, Dhruv Kumar +1 more
Large Language Models (LLMs) have demonstrated significant potential in automated software security, particularly in vulnerability detection....
Zhenlei Ye, Xiaobing Sun, Sicong Cao +2 more
The advances of large language models (LLMs) have paved the way for automated software vulnerability repair approaches, which iteratively refine the...
Liming Lu, Xiang Gu, Junyu Huang +5 more
Large Language Models (LLMs) are increasingly used in agentic systems, where their interactions with diverse tools and environments create complex,...
Zhang Wei, Peilu Hu, Zhenyuan Wei +16 more
The increasing deployment of large language models (LLMs) in safety-critical applications raises fundamental challenges in systematically evaluating...
Safwan Shaheer, G. M. Refatul Islam, Mohammad Rafid Hamid +1 more
In this fast-evolving area of LLMs, our paper discusses the significant security risk presented by prompt injection attacks. It focuses on small...
Chaomeng Lu, Bert Lagaisse
Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiveness...
Devanshu Sahoo, Vasudev Majhi, Arjun Neekhra +3 more
The use of Large Language Models (LLMs) as automatic judges for code evaluation is becoming increasingly prevalent in academic environments. But...
Futa Waseda, Shojiro Yamabe, Daiki Shiono +2 more
Large vision-language models (LVLMs) are vulnerable to typographic attacks, where misleading text within an image overrides visual understanding....
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 3,371+ 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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