AI Threat AlertSurveyMEDIUM
MutMem: Cryptographically Authorized Mutation in Persistent Agent Memory
Walid Saidi
Persistent agent memory must adapt as later outcomes change earlier evidence, yet mutable retrieval weights create an attribution problem: reviewers...
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 61–80 of 107 papers
Clear filtersSurveyMEDIUM
PRWeaver: Evaluating LLM-Based Code Auditors against Long-Horizon Malicious Pull Requests
Yuekun Wang, Mingfei Cheng, Xiaofei Xie
LLM-based code auditors are increasingly integrated into pull-request (PR) workflows, yet their reliability against adversarial changes distributed...
SurveyHIGH
SoK: Intent-Oriented Systematization of Multi-Turn LLM Jailbreaks
Siyuan Li, Aodu Wulianghai, Zehao Liu +8 more
Large Language Models (LLMs) are increasingly deployed in interactive settings, where user intent commonly unfolds through multi-turn dialogue....
SurveyLOW
Impossible to hide secret ...: Uncovering Security and Privacy Issues in LLM-native IDEs
Mostafijur Rahman Akhond, Md Afif Al Mamun, Gias Uddin +1 more
LLM-native IDEs (Integrated Development Environments), aka LIDEs, are designed from the ground up to work with Large Language Models (LLMs). LIDEs...
SurveyMEDIUM
MemPoison: Uncovering Persistent Memory Threats and Structural Blind Spots in LLM Agents
Jifeng Gao, Kang Xia, Yi Zhang +5 more
Persistent external memory enhances agent continuity but introduces persistent security vulnerabilities: adversarial content can be injected via...
SurveyMEDIUM
How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement
Alexandra E. Michael, Franziska Roesner
As AI agents gain prevalance, users are increasingly exposed to the risks such systems entail. Prompt injection attacks, as well as hallucination,...
SurveyMEDIUM
Isolation as a First-Class Principle for LLM-Agent System Safety: Concepts, Taxonomy, Challenges and Future Directions
Huihao Jing, Wenbin Hu, Shaojin Chen +10 more
The capability of LLM agents to function as the ``brain'' of a system fundamentally expands the scope of analysis beyond a standalone model....
SurveyMEDIUM
From Legacy Documentation to OSCAL: An MCP-Based Agent Pipeline for Threat-Informed Continuous Compliance in Critical Infrastructure
Lea Roxanne Muth, Marian Margraf
In critical infrastructure, operational technology environments often cannot be actively scanned, and yet active system feedback is needed for risk...
SurveyLOW
Progressive Crystallization: Turning Agent Exploration into Deterministic, Lower-Cost Workflows in Production
Arun Malik
AI agents deployed for IT operations are typically permanent cost centers because every execution requires full LLM inference, even for previously...
SurveyMEDIUM
Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies
Kiarash Ahi, Saeed Valizadeh
Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI,...
SurveyHIGH
Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis
Mingchen Li, Meikang Qiu, Zifan Peng +4 more
Large language model (LLM)-assisted software security operates at a difficult boundary: the vulnerability-analysis terminology needed for legitimate...
SurveyHIGH
From Battlefield to Boardroom: Strategic Red Teaming as an Epistemic Governance Instrument in the Age of AI
Jeroen Janssen
Organizations increasingly make strategic decisions about AI systems whose behaviour, failure modes, and institutional effects cannot be fully known...
SurveyMEDIUM
Generative AI and Federated Learning for Intrusion Detection Systems: A Survey
Jiefei Liu, Abu Saleh Md Tayeen, Pratyay Kumar +5 more
Intrusion Detection Systems (IDSs) are essential for monitoring network traffic and identifying malicious activities in modern cyber-physical,...
SurveyHIGH
A Lifecycle and Application-Stack Survey of Large Language Model Vulnerabilities: Attacks, Risks, Defenses, and Open Problems
Seyed Bagher Hashemi Natanzi, Bo Tang
Large language models are no longer only text generators. They are increasingly embedded in retrieval pipelines, enterprise assistants, coding...
SurveyMEDIUM
Beyond Wireless Security: Covert Communications in Large Language Model-enabled Edge Networks
Yuanai Xie, Jiaxin Chen, Zhaozhi Liu
Large language model (LLM)-enabled edge networks (LLMENs) offer mobile users high-quality and low-latency AI-generated content services in the 6G...
SurveyMEDIUM
EvalSafetyGap: A Hybrid Survey and Conceptual Framework for LLM Evaluation-Safety Failures
Buğra Alperen Uluırmak, Rifat Kurban
LLM evaluation and AI safety face a shared measurement problem: benchmark scores, reward-model signals, and reported safety metrics can improve while...
SurveyMEDIUM
Agents That Know Too Much: A Data-Centric Survey of Privacy in LLM Agents
Nada Lahjouji, Ashwin Gerard Colaco
Large language model agents increasingly query databases, search document collections, call external APIs, remember past interactions, and act on a...
SurveyHIGH
Adversarial Diffusion Across Modalities: A Fusion Survey of Attacks, Defenses, and Evaluation for Text, Vision, and Vision-Language Models
Abrar Alotaibi, Moataz Ahmed
Adversarial evaluation of AI systems has matured along four largely disconnected tracks: diffusion-based attacks on text and large language models...
SurveyMEDIUM
CyberChainBench: Can AI Agents Secure Smart Contracts Against Real-World On-Chain Vulnerabilities?
Jintao Huang, Fengqing Jiang, Radha Poovendran +1 more
We present CyberChainBench, a benchmark for evaluating LLM-based agents on smart contract security across three complementary tasks: vulnerability...
SurveyMEDIUM
LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges
Thi Huyen Nguyen, Zahra Ahmadi
The rapid growth of scientific submissions has pushed traditional peer review toward its scalability limits, motivating the exploration of large...
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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