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 84 papers
Clear filtersPinran Gao, Lingxiang Wang, Ying Zhang +1 more
The rapid integration of large language models (LLMs) into mobile applications has introduced a new class of credential security risk: leaked...
Malikeh Ehghaghi, Boglárka Ecsedi, Marsha Chechik +1 more
Adversarial robustness evaluations of large language models (LLMs) typically report attack success rate (ASR) under fixed query budgets, implicitly...
Naihao Deng, Yilun Zhu, Naichen Shi +2 more
Warning: This paper contains several toxic and offensive statements. Modern large language models (LLMs) are typically aligned through large-scale...
Yuchen Chen, Weisong Sun, Haocheng Huang +11 more
Code Language Models (CodeLMs) have become integral to software engineering, significantly advancing code intelligence tasks. However, their...
Yuchen Ling, Shengcheng Yu, Zhenyu Chen +1 more
Large language model (LLM) agents are rapidly moving from conversational interfaces to software components that plan, invoke tools, maintain memory,...
Peiyang Li, Songping Wang, Yi Huang +9 more
Autonomous AI agents have driven the transition from conversation to task execution, shifting security failures from textual deception to system...
Saeid Jamshidi, Amin Nikanjam, Arghavan Moradi Dakhel +2 more
Large Language Models (LLMs) in multi-turn interactions maintain evolving context rather than generating isolated responses, making them vulnerable...
Carlos S. Sepúlveda, Gonzalo A. Ruz
Neural combinatorial optimization (NCO) trains autoregressive policies to solve routing problems. The standard training algorithm, REINFORCE with a...
Aniket 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....
Shixiong Jiang, Taozheng Zhu, Fanxin Kong
Offline safe reinforcement learning (Safe RL) enables policy learning without online interactions, making it suitable for safety-critical systems...
Yinan Wang
AI Scientist agents are often evaluated as if capability were mainly a function of model quality, prompting, or reasoning scaffolds. We test a...
Ethan Rathbun, Ahmed Agha, Saaduddin Mahmud +3 more
World models have recently seen a rapid growth in both their popularity and capability as more data efficient tools for generating robot training...
Charles Westphal, Timothy Douglas, Keivan Navaie +2 more
Large language models can be fine-tuned to encode prompt-borne secrets into fluent, seemingly benign outputs. This creates a steganographic...
Srravya Chandhiramowuli, Ding Wang, Alex Taylor
We present an ethnographic study of an alternative approach to data work, developed by a civic-tech initiative that builds datasets for training and...
Bartłomiej Marek, Lorenzo Rossi, Vincent Hanke +4 more
Recent work has applied differential privacy (DP) to adapt large language models (LLMs) for sensitive applications, offering theoretical guarantees....
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...
Ziqian Zhong, Ivgeni Segal, Ivan Bercovich +3 more
Agent benchmarks score submissions with outcome verifiers that are typically hand-written and brittle, leaving them open to reward hacking. We audit...
Victor De Marez, Luna De Bruyne, Walter Daelemans
Factual sycophancy occurs when a language model abandons a correct, verifiable answer under social pressure. Because a flip occurs only when pressure...
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,...
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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