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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Date
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Showing 81–100 of 252 papers

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SurveyLOW

Critique of Agent Model

Eric Xing, Mingkai Deng, Jinyu Hou

What is an agent? What constitutes agency? With the rise of Large Language Model (LLM) systems marketed as ``coding agents'', ``AI co-scientists'',...

4 months agocs.AIcs.LGcs.MAPDF
SurveyMEDIUM

One Year Later...The Harms Persist, But So Do We!

Annika Marie Schoene, Cansu Canca, Gautham Vijay Kumar +1 more

General-purpose large language models (LLMs) are increasingly used for mental health-related conversations, yet safety safeguards remain inadequate...

4 months agocs.CLcs.AIPDF
SurveyMEDIUM

Runtime Compliance Verification for AI Agents

Nafiseh Kahani, Masoud Barati, Diana Addae

AI agents now handle personal data through tool use, function calls, and multi turn dialogue, which can create obligations under the General Data...

4 months agocs.SEPDF
SurveyMEDIUM

SoK: AI-Augmented Binary Reversing

Yujeong Kwon, Yiyue Zhang, Shakhzod Yuldoshkhujaev +3 more

Binary reversing is fundamental to software understanding, vulnerability discovery, malware investigation, and firmware auditing. However, it remains...

5 months agocs.CRcs.AIcs.SEPDF
SurveyLOW

The Chronicles of Radio Frequency Fingerprinting

Abdul Aziz, Ingrid Huso, Savio Sciancalepore +1 more

Radio Frequency Fingerprinting (RFF) has evolved from an early idea for radar emitter identification into a broad research field for wireless device...

5 months agocs.CRPDF
SurveyMEDIUM

SecureClaw: Clawing Back Control of LLM Agents

Yuhan Ma, Stefan Schmid

Tool-using large language model (LLM) agents face two distinct security failures: unauthorized external actions and exposure of sensitive plaintext...

5 months agocs.CRcs.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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