AI Threat Alert
Attack MEDIUM
Agents of Chaos
Natalie Shapira, Chris Wendler, Avery Yen +35 more
We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent...
AI Security Research
AI Threat Alert indexes 3,397+ 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
- Total3,397
- Attack1,314
- Benchmark975
- Defense477
- Tool341
- Survey196
Showing 241–260 of 458 papers
Clear filters Attack MEDIUM
vLLM Semantic Router: Signal Driven Decision Routing for Mixture-of-Modality Models
Xunzhuo Liu, Huamin Chen, Samzong Lu +27 more
As large language models (LLMs) diversify across modalities, capabilities, and cost profiles, the problem of intelligent request routing -- selecting...
Attack MEDIUM
Efficient Multi-Party Secure Comparison over Different Domains with Preprocessing Assistance
Kaiwen Wang, Xiaolin Chang, Yuehan Dong +1 more
Secure comparison is a fundamental primitive in multi-party computation, supporting privacy-preserving applications such as machine learning and data...
Attack MEDIUM
AndroWasm: an Empirical Study on Android Malware Obfuscation through WebAssembly
Diego Soi, Silvia Lucia Sanna, Lorenzo Pisu +2 more
In recent years, stealthy Android malware has increasingly adopted sophisticated techniques to bypass automatic detection mechanisms and harden...
Attack MEDIUM
DeepContext: Stateful Real-Time Detection of Multi-Turn Adversarial Intent Drift in LLMs
Justin Albrethsen, Yash Datta, Kunal Kumar +1 more
While Large Language Model (LLM) capabilities have scaled, safety guardrails remain largely stateless, treating multi-turn dialogues as a series of...
Attack MEDIUM
Policy Compiler for Secure Agentic Systems
Nils Palumbo, Sarthak Choudhary, Jihye Choi +2 more
LLM-based agents are increasingly being deployed in contexts requiring complex authorization policies: customer service protocols, approval...
Attack MEDIUM
From Tool Orchestration to Code Execution: A Study of MCP Design Choices
Yuval Felendler, Parth A. Gandhi, Idan Habler +2 more
Model Context Protocols (MCPs) provide a unified platform for agent systems to discover, select, and orchestrate tools across heterogeneous execution...
Attack MEDIUM
SuperLocalMemory: Privacy-Preserving Multi-Agent Memory with Bayesian Trust Defense Against Memory Poisoning
Varun Pratap Bhardwaj
We present SuperLocalMemory, a local-first memory system for multi-agent AI that defends against OWASP ASI06 memory poisoning through architectural...
Attack MEDIUM
Closing the Distribution Gap in Adversarial Training for LLMs
Chengzhi Hu, Jonas Dornbusch, David Lüdke +2 more
Adversarial training for LLMs is one of the most promising methods to reliably improve robustness against adversaries. However, despite significant...
Attack MEDIUM
Overthinking Loops in Agents: A Structural Risk via MCP Tools
Yohan Lee, Jisoo Jang, Seoyeon Choi +2 more
Tool-using LLM agents increasingly coordinate real workloads by selecting and chaining third-party tools based on text-visible metadata such as tool...
Attack MEDIUM
MCPShield: A Security Cognition Layer for Adaptive Trust Calibration in Model Context Protocol Agents
Zhenhong Zhou, Yuanhe Zhang, Hongwei Cai +6 more
The Model Context Protocol (MCP) standardizes tool use for LLM-based agents and enable third-party servers. This openness introduces a security...
Attack MEDIUM
Anticipating Adversary Behavior in DevSecOps Scenarios through Large Language Models
Mario Marín Caballero, Miguel Betancourt Alonso, Daniel Díaz-López +3 more
The most valuable asset of any cloud-based organization is data, which is increasingly exposed to sophisticated cyberattacks. Until recently, the...
Attack MEDIUM
OMNI-LEAK: Orchestrator Multi-Agent Network Induced Data Leakage
Akshat Naik, Jay Culligan, Yarin Gal +4 more
As Large Language Model (LLM) agents become more capable, their coordinated use in the form of multi-agent systems is anticipated to emerge as a...
Attack MEDIUM
In-Context Autonomous Network Incident Response: An End-to-End Large Language Model Agent Approach
Yiran Gao, Kim Hammar, Tao Li
Rapidly evolving cyberattacks demand incident response systems that can autonomously learn and adapt to changing threats. Prior work has extensively...
Attack MEDIUM
TensorCommitments: A Lightweight Verifiable Inference for Language Models
Oguzhan Baser, Elahe Sadeghi, Eric Wang +5 more
Most large language models (LLMs) run on external clouds: users send a prompt, pay for inference, and must trust that the remote GPU executes the LLM...
Attack MEDIUM
Vulnerabilities in Partial TEE-Shielded LLM Inference with Precomputed Noise
Abhishek Saini, Haolin Jiang, Hang Liu
The deployment of large language models (LLMs) on third-party devices requires new ways to protect model intellectual property. While Trusted...
Attack MEDIUM
A Behavioral Fingerprint for Large Language Models: Provenance Tracking via Refusal Vectors
Zhenyu Xu, Victor S. Sheng
Protecting the intellectual property of large language models (LLMs) is a critical challenge due to the proliferation of unauthorized derivative...
Attack MEDIUM
LLMs + Security = Trouble
Benjamin Livshits
We argue that when it comes to producing secure code with AI, the prevailing "fighting fire with fire" approach -- using probabilistic AI-based...
Attack MEDIUM
Reverse-Engineering Model Editing on Language Models
Zhiyu Sun, Minrui Luo, Yu Wang +2 more
Large language models (LLMs) are pretrained on corpora containing trillions of tokens and, therefore, inevitably memorize sensitive information....
Attack MEDIUM
AgentSys: Secure and Dynamic LLM Agents Through Explicit Hierarchical Memory Management
Ruoyao Wen, Hao Li, Chaowei Xiao +1 more
Indirect prompt injection threatens LLM agents by embedding malicious instructions in external content, enabling unauthorized actions and data theft....
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 3,397+ 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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