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
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Showing 1901–1920 of 2,152 papers

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BenchmarkMEDIUM

Evaluating Control Protocols for Untrusted AI Agents

Jon Kutasov, Chloe Loughridge, Yuqi Sun +4 more

As AI systems become more capable and widely deployed as agents, ensuring their safe operation becomes critical. AI control offers one approach to...

1 years agocs.AIPDF
AttackMEDIUM

ShadowLogic: Backdoors in Any Whitebox LLM

Kasimir Schulz, Amelia Kawasaki, Leo Ring

Large language models (LLMs) are widely deployed across various applications, often with safeguards to prevent the generation of harmful or...

1 years agocs.CRcs.AIPDF
DefenseMEDIUM

Reimagining Safety Alignment with An Image

Yifan Xia, Guorui Chen, Wenqian Yu +3 more

Large language models (LLMs) excel in diverse applications but face dual challenges: generating harmful content under jailbreak attacks and...

1 years agocs.AIcs.CRPDF
AttackMEDIUM

Diffusion LLMs are Natural Adversaries for any LLM

David Lüdke, Tom Wollschläger, Paul Ungermann +2 more

We introduce a novel framework that transforms the resource-intensive (adversarial) prompt optimization problem into an \emph{efficient, amortized...

1 years agocs.LGstat.MLPDF
BenchmarkMEDIUM

Self-HarmLLM: Can Large Language Model Harm Itself?

Heehwan Kim, Sungjune Park, Daeseon Choi

Large Language Models (LLMs) are generally equipped with guardrails to block the generation of harmful responses. However, existing defenses always...

1 years agocs.CLcs.AIPDF

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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