AI Threat Alert
Survey MEDIUM
AprielGuard
Jaykumar Kasundra, Anjaneya Praharaj, Sourabh Surana +11 more
Safeguarding large language models (LLMs) against unsafe or adversarial behavior is critical as they are increasingly deployed in conversational and...
AI Security Research
AI Threat Alert indexes 3,771+ 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.
- Adversarial attacks
- Model defenses
- Red-teaming benchmarks
- Surveys
- Security tooling
- Total3,771
- Attack1,458
- Benchmark1,072
- Defense533
- Tool380
- Survey220
Showing 2761–2780 of 3,771 papers
Benchmark HIGH
Well Begun is Half Done: Location-Aware and Trace-Guided Iterative Automated Vulnerability Repair
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...
Benchmark MEDIUM
Optimistic TEE-Rollups: A Hybrid Architecture for Scalable and Verifiable Generative AI Inference on Blockchain
Aaron Chan, Alex Ding, Frank Chen +3 more
The rapid integration of Large Language Models (LLMs) into decentralized physical infrastructure networks (DePIN) is currently bottlenecked by the...
Tool HIGH
Odysseus: Jailbreaking Commercial Multimodal LLM-integrated Systems via Dual Steganography
Songze Li, Jiameng Cheng, Yiming Li +2 more
By integrating language understanding with perceptual modalities such as images, multimodal large language models (MLLMs) constitute a critical...
Attack MEDIUM
AI Security Beyond Core Domains: Resume Screening as a Case Study of Adversarial Vulnerabilities in Specialized LLM Applications
Honglin Mu, Jinghao Liu, Kaiyang Wan +4 more
Large Language Models (LLMs) excel at text comprehension and generation, making them ideal for automated tasks like code review and content...
Other MEDIUM
On the Effectiveness of Instruction-Tuning Local LLMs for Identifying Software Vulnerabilities
Sangryu Park, Gihyuk Ko, Homook Cho
Large Language Models (LLMs) show significant promise in automating software vulnerability analysis, a critical task given the impact of security...
Attack MEDIUM
IoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense
Rahul Yumlembam, Biju Issac, Seibu Mary Jacob +1 more
Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years,...
Tool MEDIUM
ReGAIN: Retrieval-Grounded AI Framework for Network Traffic Analysis
Shaghayegh Shajarian, Kennedy Marsh, James Benson +2 more
Modern networks generate vast, heterogeneous traffic that must be continuously analyzed for security and performance. Traditional network traffic...
Attack MEDIUM
Conditional Adversarial Fragility in Financial Machine Learning under Macroeconomic Stress
Samruddhi Baviskar
Machine learning models used in financial decision systems operate in nonstationary economic environments, yet adversarial robustness is typically...
Benchmark MEDIUM
A Multi-Perspective Benchmark and Moderation Model for Evaluating Safety and Adversarial Robustness
Naseem Machlovi, Maryam Saleki, Ruhul Amin +5 more
As large language models (LLMs) become deeply embedded in daily life, the urgent need for safer moderation systems that distinguish between naive and...
Benchmark MEDIUM
GuardEval: A Multi-Perspective Benchmark for Evaluating Safety, Fairness, and Robustness in LLM Moderators
Naseem Machlovi, Maryam Saleki, Ruhul Amin +5 more
As large language models (LLMs) become deeply embedded in daily life, the urgent need for safer moderation systems, distinguishing between naive from...
Attack MEDIUM
SafeMed-R1: Adversarial Reinforcement Learning for Generalizable and Robust Medical Reasoning in Vision-Language Models
A. A. Gde Yogi Pramana, Jason Ray, Anthony Jaya +1 more
Vision--Language Models (VLMs) show significant promise for Medical Visual Question Answering (VQA), yet their deployment in clinical settings is...
Attack HIGH
Causal-Guided Detoxify Backdoor Attack of Open-Weight LoRA Models
Linzhi Chen, Yang Sun, Hongru Wei +1 more
Low-Rank Adaptation (LoRA) has emerged as an efficient method for fine-tuning large language models (LLMs) and is widely adopted within the...
Attack HIGH
GShield: Mitigating Poisoning Attacks in Federated Learning
Sameera K. M., Serena Nicolazzo, Antonino Nocera +2 more
Federated Learning (FL) has recently emerged as a revolutionary approach to collaborative training Machine Learning models. In particular, it enables...
Other LOW
JEPA-Reasoner: Decoupling Latent Reasoning from Token Generation
Bingyang Kelvin Liu, Ziyu Patrick Chen, David P. Woodruff
Current autoregressive language models couple high-level reasoning and low-level token generation into a single sequential process, making the...
Defense MEDIUM
Elevating Intrusion Detection and Security Fortification in Intelligent Networks through Cutting-Edge Machine Learning Paradigms
Md Minhazul Islam Munna, Md Mahbubur Rahman, Jaroslav Frnda +2 more
The proliferation of IoT devices and their reliance on Wi-Fi networks have introduced significant security vulnerabilities, particularly the KRACK...
Benchmark HIGH
DREAM: Dynamic Red-teaming across Environments for AI Models
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,...
Attack HIGH
PromptScreen: Efficient Jailbreak Mitigation Using Semantic Linear Classification in a Multi-Staged Pipeline
Akshaj Prashanth Rao, Advait Singh, Saumya Kumaar Saksena +1 more
Prompt injection and jailbreaking attacks pose persistent security challenges to large language model (LLM)-based systems. We present PromptScreen,...
Defense MEDIUM
R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression
Kun Zhao, Siyuan Dai, Yingying Zhang +9 more
Early detection of Alzheimer's disease (AD) requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic...
Benchmark HIGH
Learning-Based Automated Adversarial Red-Teaming for Robustness Evaluation of Large Language Models
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...
Frequently asked questions
What is AI security research?
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.
How many AI security papers does AI Threat Alert track?
AI Threat Alert indexes 3,771+ papers on AI/ML security, classified across attack, defense, benchmark, survey, and tool categories and updated continuously.
Where do the research papers come from?
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.
What topics does the AI security research cover?
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.
How is this different from a generic paper search?
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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