AI Threat Alert
Benchmark MEDIUM
Human-on-the-Bridge: Scalable Evaluation for AI Agents
Fouad Bousetouane
AI agents must be evaluated as behavioral systems, not as isolated response generators. They reason across turns, call tools, preserve context,...
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 501–520 of 888 papers
Clear filters Benchmark MEDIUM
The Art of Mixology: Mixup-based Obfuscation for Privacy-Preserving Split Learning in Large Language Models
Chen Chen, Xiang Gao, Xianshun Wang +6 more
Split learning provides a practical paradigm for resource-constrained users to train Large Language Models (LLMs) by offloading computation-intensive...
Attack MEDIUM
Cross-Silo De-Anonymization Under Local Differential Privacy: Threat Model, Phase Transition, and Coordination Necessity
Ziniu Liu, Aiping Li
When a person's records appear in k independent data silos, each protected by (epsilon, delta)-differential privacy, standard composition yields a...
Benchmark MEDIUM
Robust and Automated Reconfiguration of Byzantine Wide-Area Replication
Rowdy Chotkan, Bulat Nasrulin, Johan Pouwelse +1 more
Distributed systems handle adversarial nodes through redundancy, which imposes a significant performance overhead. In blockchain systems, Byzantine...
Benchmark MEDIUM
An Evaluation of Data Leakage Risks in Tool-Using LLM Agents in Realistic Scenarios
Hankyul Baek, Jaewon Noh, Sang Seo +5 more
AI agents are increasingly being adopted in enterprise and personal settings with access to emails, databases, documents, and other tools where they...
Attack MEDIUM
The Proxy Knows Too Much: Sealing LLM API Routers with Attested TEEs
Sipeng Xie, Qianhong Wu, Hengrun Lu +4 more
Agents increasingly access large language models (LLMs) through API routers. A router terminates the client's transport-layer security session and...
Attack MEDIUM
Gaming-Resistant Insurance Contracts for Autonomous AI Agents: Strategy-Proof Toll Mechanism Design
Hao-Hsuan Chen
Paper A defines a time-consistent actuarial runtime that prices each side-effect-bearing action against a contractually fixed safe default and gates...
Attack MEDIUM
Invisible Manipulation Channels in AI-Assisted Financial Advisory: Implications for Market Integrity and Regulatory Design
Liuyang Yao, Zhouyu Li, Junguang He +1 more
AI systems are increasingly deployed for credit assessment and investment advisory in global financial markets, yet the integrity of their inference...
Benchmark MEDIUM
Your "Pro" LLM Subscription May Actually Be "Free": Exposing Fingerprint Spoofing Risks in LLM Inference Services
Jiahao Zhang, Xiuyu Li, Suhang Wang
As Large Language Model (LLM) APIs become ubiquitous, users increasingly rely on black-box fingerprinting to verify that providers are serving the...
Benchmark MEDIUM
SkillVetBench: LLM-as-Judge for Multi-Dimensional Security Risk Evaluation in Open-Source LLM Agent Skills
Ismail Hossain, Sai Puppala, Md Jahangir Alam +2 more
Open-source LLM agent ecosystems are growing rapidly, yet the security of community-contributed skills - modular tool definitions that extend agent...
Survey MEDIUM
Snyk VulnBench JS 1.0: Can LLMs Find the Same Bugs Twice?
Liran Tal, Johannes Kloos, Arsenii Rudich +2 more
We ran 300 repeated vulnerability-finding scans to measure how repeatable agentic large language model (LLM) security review is on the same...
Attack MEDIUM
Beyond Runtime Enforcement: Shield Synthesis as Defensibility Analysis for Adversarial Networks
Achraf Hsain, Sultan Almuhammadi
Shielded reinforcement learning is typically presented as a runtime safety mechanism that compiles temporal-logic specifications into automata...
Tool MEDIUM
The Emergence of Autonomous Penetration Capabilities in Large Language Model-Powered AI Systems
Jiaqi Luo, Jiarun Dai, Zhile Chen +8 more
Nowadays, the autonomous execution of cyberattacks capable of causing substantial real-world harm is widely regarded as one of the critical red lines...
Survey MEDIUM
No Hidden Prompts Needed! You Can Game AI Peer Review with Presentation-Only Revisions
Xu Yang, Zhizhou Sha, Junbo Li +10 more
As AI-generated reviews move from experimental tools into peer-review infrastructure, most robustness concerns have focused on explicit attacks such...
Attack MEDIUM
Nous: An Attempt to Extract and Inject the Cognition Behind Prediction-Market Behavior
Haowei Qian
As LLM agents proliferate in prediction markets and collective decision-making, they risk a cognitive monoculture: agents built on shared foundation...
Defense MEDIUM
PolicyGuard: Towards Test-time and Step-level Adversary Defense for Reinforcement Learning Agent
Junfeng Guo Heng Huang
While real-world applications of reinforcement learning (RL) are becoming increasingly popular, the security of RL systems deserve more attention and...
Tool MEDIUM
The Containment Gap: How Deployed Agentic AI Frameworks Fail Public-Facing Safety Requirements
Md Jafrin Hossain, Mohammad Arif Hossain, Weiqi Liu +1 more
Agentic large language model systems that autonomously invoke tools, maintain persistent memory, and execute multi-step plans are increasingly...
Benchmark MEDIUM
Prefill Awareness in Large Language Models
Andy Wang, Parv Mahajan, David Demitri Africa +3 more
Safety-relevant studies of language models, including alignment and jailbreaking evaluations and AI control protocols, often rely on prefilling model...
Benchmark MEDIUM
Smarter Saboteurs, Better Fixers: Scaling & Security in Linear Multi-Agent Workflows
Timothy McAllister, Sina Abdidizaji, Ivan Garibay +1 more
As LLM-based multi-agent systems (MAS) are deployed in the wild, the resilience of their collaboration structures against adversarial compromise...
Tool MEDIUM
SMSR: Certified Defence Against Runtime Memory Poisoning in Persistent LLM Agent Systems
Tarun Sharma
Retrieval-augmented generation (RAG) agents increasingly run with persistent memory that accumulates across user sessions. This creates a new attack...
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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