AiMafia: We Secure What Others Fear to Touch
An independent collective of AI security researchers — built for the age of advanced systems. We study emerging threats, publish findings, and share practical knowledge to strengthen the wider community.
Read Our Research
Who We Are
Cybersecurity Veterans. AI Security Researchers.
AiMafia wasn't assembled overnight. We're an independent collective of battle-tested cybersecurity professionals studying the AI revolution as it unfolds — and documenting the risks before they scale. Our team brings decades of combined experience in offensive security, threat intelligence, red teaming, and enterprise risk management.
When the industry pivoted to AI, most security frameworks didn't follow. We did. AiMafia exists at the intersection of deep security expertise and cutting-edge AI systems research — surfacing findings, testing assumptions, and helping close the gap that legacy approaches still struggle to address.
What Sets Us Apart
  • Born from the cybersecurity trenches
  • Researchers and practitioners
  • AI-native threat modeling
  • Red team DNA in everything we study
  • Zero tolerance for security theater
The Problem
AI Is Moving Fast. Security Is Barely Keeping Up.
Every week, organizations deploy new AI models, agents, and pipelines — often with little to no security validation. The attack surface is expanding faster than defenders can map it. AiMafia was formed precisely because this gap is dangerous, and most security research groups don't have the depth to close it.
The AI Journey
Security Across the Entire AI Lifecycle
AI security isn't a single checkpoint — it's a continuous research discipline that spans every phase of development, deployment, and operation. AiMafia studies how threats emerge at each stage and shares findings to help harden AI systems everywhere.
From the first architectural decision to real-time threat response in production, we track how risks evolve across the AI lifecycle and publish research to help the community harden systems at every layer — not just at the perimeter.
Core Research
What We Study
AI Red Teaming
We probe AI systems to understand how they fail under adversarial pressure. Our research examines prompt injection, model extraction, adversarial inputs, and logic manipulation to document real-world exposure.
AI Pipeline Security
Training pipelines, data ingestion workflows, and model registries are high-value attack surfaces. We study the full MLOps chain — from raw data to model serving — and publish findings on how to harden it.
Threat Modeling for AI
Standard STRIDE doesn't fully capture AI systems. We develop AI-specific threat frameworks that account for model behavior, inference risks, and supply chain vulnerabilities unique to machine learning.
Local & Edge Inference Security
We study the risks that emerge when AI runs on-device or at the edge, including model tampering, side-channel attacks, and hardware-level vulnerabilities. Our research focuses on the unique challenges of securing inference outside controlled cloud environments.
The Threat Landscape
AI Attack Vectors You Can't Afford to Ignore
These aren't theoretical risks. They are active attack techniques being deployed against production AI systems today. AiMafia has mapped, tested, and developed mitigations for each — and we stay ahead of the evolving threat landscape so you don't have to.
Who We Learn With
Built for Teams and Researchers Who Take AI Security Seriously
CISOs & Security Leaders
Tracking emerging AI threats and looking for credible research that sharpens defensive strategy.
AI & ML Engineers
Building models and pipelines who want security guidance grounded in real findings and practical analysis.
Enterprise Technology Teams
Evaluating AI systems at scale and using research to identify gaps before attackers do.
AI-Native Builders
Moving fast and sharing a commitment to open research, peer learning, and responsible security practices.
Our Edge
Why AiMafia. Why Now.
The Old Way
  • Generic security frameworks not designed for AI
  • Consultants who've never touched an ML pipeline
  • Compliance-focused, risk-averse recommendations
  • Slow analysis that lags behind deployment velocity
  • Findings that gather dust instead of driving action
The AiMafia Way
  • AI-native security research built from the ground up
  • Researchers with hands-on AI and offensive security experience
  • Risk-driven, technically precise findings
  • Agile analysis that matches the pace of modern AI systems
  • Actionable publications that get implemented, not archived