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.
Showing 41–60 of 123 papers
Clear filtersHui Xuan Tan, Stefanos Chaliasos, Alastair F. Donaldson
A zero-knowledge virtual machine (zkVM) lets an untrusted prover convince a verifier that a program, typically Rust compiled to RISC-V, ran...
Yao Wang, Zehua Ma, Weiming Zhang +1 more
Indirect Prompt Injection (IPI) poses a critical threat to LLM agents. Existing defenses largely focus on whether an injection induces a new tool...
Ilan Zendel, Matan Levy, Stav Cohen +2 more
LLM agents increasingly act on information retrieved from public sources they treat as credible, which turns a manipulated recommendation into a...
Binhao Ma, Weikang Ding, Hanqing Guo +5 more
Speech watermarking is becoming a key defense for digital copyright and content authentication. Existing attacks treat evasion as a removal problem:...
Shravya Kanchi
Multi-agent LLM systems place several agents on shared infrastructure, where each agent holds permissions far broader than the task it is delegated...
Datta Manikanta Sri Hari Danduri, Connor Everett Glosner, Aravind Machiry
Securing bootloaders is critical to establishing the trusted computing base of modern systems. TianoCore's edk2, the de facto UEFI-based bootloader,...
Wei-Lin Chen, Zhepei Wei, Xinyu Zhu +3 more
Large language model (LLM) coding agents increasingly operate with shell access, allowing them to write scripts, move files, alter permissions, and...
Farooq Shaikh, Biagio Montaruli
The Model Context Protocol (MCP) lets agentic AI frameworks connect language models to external tool providers, but it carries attacker-controlled...
Hasan Kaplan, Indika Kumara, Willem-Jan van den Heuvel +1 more
Large language models (LLMs) using retrieval-augmented generation (RAG) can synthesize Amazon Web Services (AWS) Identity and Access Management (IAM)...
Yiwei Zha, Qingyu Meng, Herbert Bos +1 more
LLM agents compact long histories to fit their context windows, but the compacted context must still carry the operating rules that govern later...
Junyu Lin, Mengjie Wu, Yonghao Dong +11 more
As streaming generation becomes the dominant serving paradigm for both text-only and multimodal large language models, ensuring safety throughout the...
Junyu Lin, Mengjie Wu, Yonghao Dong +11 more
As streaming generation becomes the dominant serving paradigm for both text-only and multimodal large language models, ensuring safety throughout the...
Shengyan Huo, Peihai Jiang, Xixiang Lyu +1 more
Third-party LoRA adapters introduce serious backdoor risks: malicious adapters can maintain normal task performance while embedding hidden,...
Xu Zhang, Zhichao Lu
As large language model (LLM) agents take on more complex tasks with greater autonomy, security has become a major obstacle to their broader...
Christopher Krakauer, Andreas Schaad
The Model Context Protocol (MCP) has become a leading standard for connecting Large Language Models (LLMs) to external tools, software, and data...
Haowen Dai, Alejandro Guerra-Manzanares
Indirect prompt injection creates a distinctive risk for blue-team Large Language Model (LLM) agents in Security Operations Centers (SOCs). Malicious...
Javad Mohammad Alizadeh, Huanmei Wu
LLM agents are increasingly built into data-analysis software, yet little evidence shows how reliably they preprocess clinical data or whether the...
Sreehari Sankar, Debargha Ganguly, Vikash Singh +17 more
Tool-using language-model agents often execute actions that their domain policies forbid. We present LeanGuard, a runtime that checks every proposed...
Arun B. Chandrasekaran, Damilola Popoola, Souradeep Bhattacharya +1 more
The increasing penetration of Distributed Energy Resources (DERs) in the smart grid has significantly expanded the cyber-attack surface....
Mingchen Li, Ren-Yi Huang, Dumindu Samaraweera +2 more
Edge deployment of Large Language Models requires optimization techniques that reduce memory, computation, and energy costs. However, preserving task...
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.
AI Threat Alert indexes 4,319+ papers on AI/ML security, classified across attack, defense, benchmark, survey, and tool categories and updated continuously.
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.
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.
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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