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 881–900 of 3,771 papers
Survey HIGH
How Much Can We Trust LLM Search Agents? Measuring Endorsement Vulnerability to Web Content Manipulation
Yimeng Chen, Zhe Ren, Firas Laakom +3 more
Large language model (LLM)-based search agents synthesize open-web content into actionable recommendations on behalf of users, creating a risk that...
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...
Attack HIGH
Automated jailbreak attack targeting multiple defense strategies
Qi Wang, Chengcheng Wan, Weijia He +4 more
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks. However, their safety remains a critical concern...
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 LOW
User as Code: Executable Memory for Personalized Agents
Bojie Li
A personalized AI agent needs a user memory: a persistent model of who the user is, built across many conversations and consulted on each new one....
Attack HIGH
DoubtProbe: Black-Box Jailbreak Defense via Structural Verification and Semantic Auditing
Xuanyu Yin, Yilin Jiang, Jun Zhou +3 more
As large language models (LLMs) are increasingly deployed in user-facing systems, black-box jailbreak defense has become an important practical...
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...
Tool HIGH
Rapid Poison: Practical Poisoning Attacks Against the Rapid Response Framework
David Huang, Jaewon Chang, Avidan Shah +2 more
The Rapid Response (RR) framework, deployed in production systems, including Anthropic's ASL-3 safeguards, continuously improves jailbreak-detection...
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...
Attack HIGH
Let Them Steal: Trapping Large Language Model Extraction Attacks with Knowledge Honeypot
Yuyang Dai, Yushun Dong
Large language models deployed as commercial APIs are vulnerable to model extraction attacks, while existing defenses either act too late or degrade...
Attack HIGH
GAS-Leak-LLM: Genetic Algorithm-Based Suffix Optimization for Black-Box LLM Jailbreaking
Aman Anifer, Vignesh Kumar Kembu, Vishnu M +4 more
Large Language Models (LLMs) constitute pivotal components within the AI-dominated information technology ecosystem. To mitigate risks associated...
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...
Benchmark HIGH
Who Pays the Price? Stakeholder-Centric Prompt Injection Benchmarking for Real-world Web Agents
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...
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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