AI Threat Alert

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
Type
AllAttackDefenseSurveyBenchmarkTool
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AllHighMedium
Date
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Showing 1–20 of 610 papers

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DefenseMEDIUM

Intent-Aware Protection for AI Agents

Fady Copty, Neta Haiby, Max Braitmaiere +1 more

AI agents increasingly access enterprise data, invoke powerful tools, and act with limited human oversight. Existing security defenses primarily...

2 days agocs.CRPDF
DefenseHIGH

SBVUL: Statement-Based Vulnerability Detection

Martin Pinzger, Katharina Stengg, Christian Macho +2 more

Many recent approaches for vulnerability detection use large language models (LLMs) to classify functions as vulnerable or benign, leaving developers...

1 weeks agocs.SEPDF

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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