AI Threat Alert indexes 3,023+ 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 61–80 of 1,050 papers
Clear filtersAniket Anand, Yiwei Hou, Daniel Fields +4 more
This paper presents AuditBench, a new benchmark dataset for evaluating the capabilities of LLMs at investigating security-related system audit logs....
Pengrun Huang, Chhavi Yadav, Ruihan Wu +1 more
Large language models (LLMs) are increasingly fine-tuned on domain-specific datasets that may contain sensitive, dataset-level properties. Recent...
Blake Bullwinkel, Eugenia Kim, Amanda Minnich +1 more
AI red teaming must continually adapt to evolving attackers and defenders. Reinforcement learning offers a promising approach to discovering novel...
Qin Yang, Lu Malloy, Joshua Lee +4 more
Large language model (LLM)-powered content moderation systems have become a critical defense against harmful online content. However, these systems...
Jianwei Tai
BCI-to-agent pipelines turn decoded neural activity into an authorization channel for tool-use agents, exposing a new attack surface we call...
Hyunseok Paeng
We present a reproducible failure mode of safety training in RAG-based LLM recommendation -- the Injection Paradox -- in which prompt injections...
Hyeji Choi, Yongtaek Lim, Minwoo Kim
Multilingual safety evaluation of large language models (LLMs) has predominantly relied on direct translation (DT) of English benchmarks into target...
Xiaofeng Lin, Yukai Yang, Daniel Guo +3 more
Tool-using LLM agents interact with the world through actions that persist state in artifacts (e.g., workspace files or logs). Consequently,...
Jianguo Zhu
Retrieval-augmented generation (RAG) systems often serialize user queries, retrieved documents, metadata, system labels, and task instructions into...
Kuncan Wang, Ziting Wang, Peizhuo Lv +4 more
Data agents integrate LLM-driven reasoning with relational data access, executable analytical tools, and multi-step workflow orchestration, making...
Lin-Fa Lee, Yi-Yu Chang, Chia-Mu Yu +1 more
WebMCP is a newly emerging protocol that enables websites to expose tools directly to AI agents, bypassing traditional user interfaces and...
Xi Yang, Chang Liu, Zhenglin Huang +4 more
As chatbots increasingly influence daily decision-making, their potential to produce misleading responses poses substantial risks to users. This...
Liangsheng Liu, Si Chen, Jiamin Wu +5 more
Vision-Language Models (VLMs), such as CLIP, have shown strong zero-shot generalization but remain highly vulnerable to adversarial perturbations,...
Weilin Lin, Ziqi Lin, Zhenxing Zhou +4 more
Image safety classifiers serve as a critical component of contemporary content moderation systems on the internet. However, their resilience against...
Abzal Aidakhmetov, Donato Crisostomi, Tommaso Mencattini +3 more
Activation steering has become a popular way to control Large Language Model (LLM) behavior without fine-tuning. Since the technique is...
Hassan Jalil Hadi, Rehana Yasmin, Ali Shoker
Rule-based Intrusion Detection and Prevention Systems (IDPS) offer precise attack detection as well as mitigation, however their manually crafted,...
Shuze Liu, Qianwen Guo, Yushun Dong
Large language models (LLMs) are increasingly deployed through hosted APIs, making model extraction a practical threat to model ownership and service...
Seungwon Jeong, Jiwoo Jeong, Hyeonjin Kim +2 more
As large language models (LLMs) are widely deployed, identifying their vulnerability through jailbreak attacks becomes increasingly critical....
Paulo Ricardo Ferreira Neves, Edson Rodrigues da Cruz Filho, Paulo Henrique Eleuterio Falsetti +7 more
Large Language Models (LLMs) have transformed natural language processing, but they remain vulnerable to Prompt Injection (PI) and Jailbreak (JB)...
Sepehr Dehdashtian, Jacob H Seidman, Vishnu N Boddeti +1 more
Audio deepfake detection (ADD) models are critical for countering the malicious use of text-to-speech (TTS) models. Evaluating and strengthening ADD...
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 3,023+ 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