AI Threat AlertAttackMEDIUM
MOLM: Mixture of LoRA Markers
Samar Fares, Nurbek Tastan, Noor Hussein +1 more
Generative models can generate photorealistic images at scale. This raises urgent concerns about the ability to detect synthetically generated images...
AI Security Research
AI Threat Alert indexes 4,319+ 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
- Total4,319
- Attack1,676
- Benchmark1,229
- Defense610
- Tool432
- Survey252
Showing 2101–2120 of 2,152 papers
Clear filtersBenchmarkMEDIUM
SecureBERT 2.0: Advanced Language Model for Cybersecurity Intelligence
Ehsan Aghaei, Sarthak Jain, Prashanth Arun +1 more
Effective analysis of cybersecurity and threat intelligence data demands language models that can interpret specialized terminology, complex document...
AttackMEDIUM
CHAI: Command Hijacking against embodied AI
Luis Burbano, Diego Ortiz, Qi Sun +5 more
Embodied Artificial Intelligence (AI) promises to handle edge cases in robotic vehicle systems where data is scarce by using common-sense reasoning...
AttackMEDIUM
Are Robust LLM Fingerprints Adversarially Robust?
Anshul Nasery, Edoardo Contente, Alkin Kaz +2 more
Model fingerprinting has emerged as a promising paradigm for claiming model ownership. However, robustness evaluations of these schemes have mostly...
BenchmarkMEDIUM
Fairness Testing in Retrieval-Augmented Generation: How Small Perturbations Reveal Bias in Small Language Models
Matheus Vinicius da Silva de Oliveira, Jonathan de Andrade Silva, Awdren de Lima Fontao
Large Language Models (LLMs) are widely used across multiple domains but continue to raise concerns regarding security and fairness. Beyond known...
AttackMEDIUM
DeepProv: Behavioral Characterization and Repair of Neural Networks via Inference Provenance Graph Analysis
Firas Ben Hmida, Abderrahmen Amich, Ata Kaboudi +1 more
Deep neural networks (DNNs) are increasingly being deployed in high-stakes applications, from self-driving cars to biometric authentication. However,...
AttackMEDIUM
The Impact of Scaling Training Data on Adversarial Robustness
Marco Zimmerli, Andreas Plesner, Till Aczel +1 more
Deep neural networks remain vulnerable to adversarial examples despite advances in architectures and training paradigms. We investigate how training...
AttackMEDIUM
Better Privilege Separation for Agents by Restricting Data Types
Dennis Jacob, Emad Alghamdi, Zhanhao Hu +2 more
Large language models (LLMs) have become increasingly popular due to their ability to interact with unstructured content. As such, LLMs are now a key...
SurveyMEDIUM
LLaVAShield: Safeguarding Multimodal Multi-Turn Dialogues in Vision-Language Models
Guolei Huang, Qinzhi Peng, Gan Xu +3 more
As Vision-Language Models (VLMs) move into interactive, multi-turn use, safety concerns intensify for multimodal multi-turn dialogue, which is...
SurveyMEDIUM
Where LLM Agents Fail and How They can Learn From Failures
Kunlun Zhu, Zijia Liu, Bingxuan Li +15 more
Large Language Model (LLM) agents, which integrate planning, memory, reflection, and tool-use modules, have shown promise in solving complex,...
DefenseMEDIUM
A Hybrid CAPTCHA Combining Generative AI with Keystroke Dynamics for Enhanced Bot Detection
Ayda Aghaei Nia
Completely Automated Public Turing tests to tell Computers and Humans Apart (CAPTCHAs) are a foundational component of web security, yet traditional...
ToolMEDIUM
A-MemGuard: A Proactive Defense Framework for LLM-Based Agent Memory
Qianshan Wei, Tengchao Yang, Yaochen Wang +7 more
Large Language Model (LLM) agents use memory to learn from past interactions, enabling autonomous planning and decision-making in complex...
AttackMEDIUM
PRIVMARK: Private Large Language Models Watermarking with MPC
Thomas Fargues, Ye Dong, Tianwei Zhang +1 more
The rapid growth of Large Language Models (LLMs) has highlighted the pressing need for reliable mechanisms to verify content ownership and ensure...
AttackMEDIUM
FuncPoison: Poisoning Function Library to Hijack Multi-agent Autonomous Driving Systems
Yuzhen Long, Songze Li
Autonomous driving systems increasingly rely on multi-agent architectures powered by large language models (LLMs), where specialized agents...
AttackMEDIUM
Dual Mechanisms of Value Expression: Intrinsic vs. Prompted Values in Large Language Models
Jongwook Han, Jongwon Lim, Injin Kong +1 more
Large language models can express values in two main ways: (1) intrinsic expression, reflecting the model's inherent values learned during training,...
DefenseMEDIUM
DiffuGuard: How Intrinsic Safety is Lost and Found in Diffusion Large Language Models
Zherui Li, Zheng Nie, Zhenhong Zhou +7 more
The rapid advancement of Diffusion Large Language Models (dLLMs) introduces unprecedented vulnerabilities that are fundamentally distinct from...
AttackMEDIUM
AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety Alignment of Large Reasoning Models
Zihao Zhu, Xinyu Wu, Gehan Hu +3 more
Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex problem-solving through Chain-of-Thought (CoT) reasoning. However,...
BenchmarkMEDIUM
WAREX: Web Agent Reliability Evaluation on Existing Benchmarks
Su Kara, Fazle Faisal, Suman Nath
Recent advances in browser-based LLM agents have shown promise for automating tasks ranging from simple form filling to hotel booking or online...
BenchmarkMEDIUM
An Ensemble Framework for Unbiased Language Model Watermarking
Yihan Wu, Ruibo Chen, Georgios Milis +1 more
As large language models become increasingly capable and widely deployed, verifying the provenance of machine-generated content is critical to...
DefenseMEDIUM
Policy-as-Prompt: Turning AI Governance Rules into Guardrails for AI Agents
Gauri Kholkar, Ratinder Ahuja
As autonomous AI agents are used in regulated and safety-critical settings, organizations need effective ways to turn policy into enforceable...
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 4,319+ 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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