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 21–40 of 130 papers
Clear filtersVarun Gadey, Zijie Liu, Alexandra Dmitrienko
Automated vulnerability repair has emerged as a promising direction to mitigate the growing number of software vulnerabilities. Recent advances in...
Hanwool Lee, Dasol Choi, Bokyeong Kim +2 more
Large language model (LLM) agents are increasingly proposed as supervisory components for safety-critical systems, yet their robustness under...
Chaeyun Kim, Daeyoung Park, Junghwan Kim +4 more
Existing safety benchmarks target general adversarial scenarios but miss finance-specific risks. Financial LLMs face regulatory compliance...
Zihao Wang, Yiming Li, Yutong Wu +8 more
Web agents driven by large language models (LLMs) are increasingly deployed in real-world environments, where they operate over untrusted web content...
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,...
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....
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...
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...
Michael J. Bommarito
The attack surface of a modern operating system is a haystack: thousands of signed binaries and millions of functions, almost none relevant to any...
Victor Akinode, Senyu Li, Wassim Hamidouche +3 more
Safety evaluation of Large Language Models (LLMs) remains heavily English-centric, leaving Low-Resource Languages (LRLs), particularly African ones,...
Haichuan Hu, Guoqing Xie, Quanjun Zhang +5 more
Large Language Models (LLMs) have shown promise for automated vulnerability repair (AVR), but they still face several limitations, including the lack...
Akindoyin Akinrele, Shreyank N Gowda
Prompt injection poses a critical threat to the safe deployment of large language models, yet existing detection approaches are typically evaluated...
Laura Jiang, Reza Ryan, Qian Li +1 more
Single-turn safety evaluation is a poor proxy for real fraud defense, where attackers escalate across multiple rounds. This paper evaluates fraud...
Doguhuan Yeke, Yanming Zhou, Leo Y. Lin +3 more
Recent advances in Vision-Language Models (VLMs) facilitate a new class of embodied AI systems, where these models are integrated into physical...
Simiao Liu, Fang Liu, Li Zhang +2 more
Large language model (LLM) agents are increasingly used for automated vulnerability repair (AVR), where repository-level reasoning enables them to...
Simiao Liu, Li Zhang, Fang Liu +3 more
Modern software ecosystems face a rapidly growing number of disclosed vulnerabilities, increasing the need for automated repair techniques that can...
Nils Loose, Joseph Bienhüls, Kristoffer Hempel +2 more
Automated detection of vulnerability-fixing commits (VFCs) is critical for timely security patch deployment, as advisory databases lag patch releases...
Chiyu Zhang, Huiqin Yang, Bendong Jiang +8 more
The rapid proliferation of LLM-based autonomous agents in real operating system environments introduces a new category of safety risk beyond content...
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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