AI Security Resources

Comprehensive AI threat intelligence and security resource hub for cybersecurity professionals

AI Attack Readiness Assessment

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Weekly AI Threat Intelligence

Real-time analysis of emerging AI security threats powered by xAI Grok

Weekly AI Threat Summary

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Top AI Threats (Weekly)

Threat Distribution

23 AI Attack Categories

Comprehensive threat taxonomy for modern AI systems with explanations

Adversarial ML Attacks Malicious inputs designed to fool AI models into making wrong predictions

AI Availability Attacks Disrupting AI service availability through targeted system attacks

AI Bias & Fairness Exploiting or amplifying unfair bias in AI decision-making systems

AI Data Poisoning Corrupting training data to compromise AI model behavior and decisions

AI Governance Gaps Lack of proper oversight and control mechanisms for AI systems

AI Infrastructure Compromise Attacking the underlying infrastructure supporting AI systems and operations

AI-Powered Attacks Using AI to enhance traditional cyberattacks and social engineering

AI Prompt Injection Malicious prompts that override AI system instructions and controls

AI Social Engineering Using AI to create sophisticated social manipulation and phishing attacks

AI Supply Chain Threats Compromising AI model distribution and third-party AI components

Backdoor Attacks Hidden triggers planted in AI models to cause malicious behavior

Cloud AI Misconfig Insecure cloud AI service configurations exposing data and models

Deepfake & Synthesis AI-generated fake content for fraud, impersonation, and misinformation

Differential Privacy Bypass Breaking privacy protection mechanisms in AI systems to extract sensitive data

Edge AI Security Vulnerabilities in AI systems deployed on edge devices and IoT endpoints

Ethical AI Violations Breaches of ethical AI principles and responsible development practices

MLOps Pipeline Attacks Compromising machine learning development and deployment pipelines

Model Extraction Stealing AI model functionality and intellectual property through queries

Model Inversion Attacks Extracting sensitive training data by analyzing AI model outputs

Output Manipulation Altering AI outputs to spread misinformation or cause harmful decisions

Privacy Inference Deducing private information about individuals from AI model behavior

Regulatory Compliance Violations of AI governance requirements and regulatory standards

Resource Exhaustion Overloading AI systems to cause denial of service or performance degradation

Security Resource Categories

Essential resources for understanding and defending against AI security threats

Global Standards & Governance Bodies

OECD AI Policy Observatory

Global AI policy tracker and governance framework

UNESCO AI Ethics

Global ethical guidelines for AI development

NIST AI Risk Management Framework

US standard for AI safety and governance

EU AI Act

Landmark regulation for AI systems in Europe

Global Partnership on AI (GPAI)

International initiative on responsible AI

Security & Safety Research Organizations

AI Incident Database

Partnership on AI incident tracking database

MITRE ATLAS

Adversarial Threat Landscape for AI Systems

Center for AI Safety (CAIS)

Research organization focused on AI safety

Partnership on AI (PAI)

Multi-stakeholder organization for responsible AI

CSET Georgetown

Center for Security and Emerging Technology

Technical Benchmarks & Testing

Robustness Gym

Adversarial robustness evaluation suite

CleverHans Library

Adversarial examples and defenses framework

Adversarial ML Benchmark

Standardized adversarial ML evaluation

Fairness Indicators

Google's bias detection and evaluation tools

Aequitas Toolkit

Bias auditing and fairness assessment

Industry Resources

Berryville IML

AI/ML security assessment methodologies

Microsoft Responsible AI

Enterprise responsible AI implementation

Google AI Principles

AI ethics and safety guidelines

IBM AI Ethics Board

Corporate AI governance frameworks

PwC AI Risk Management

Enterprise AI risk assessment frameworks