AI-Specific Vulnerabilities

Category Overview: Techniques targeting AI-specific vulnerabilities and model-related security issues in MCP systems.

This category covers vulnerabilities unique to AI systems, including model manipulation, inference attacks, and AI-specific exploitation techniques that target machine learning models and AI infrastructure.

Techniques in this Category

  1. Model Poisoning - Corrupting AI models through malicious training data or updates
  2. Inference Attacks - Extracting sensitive information through model inference
  3. Model Theft - Unauthorized extraction and replication of AI models
  4. Adversarial Attacks - Using adversarial inputs to manipulate AI model behavior

Common Attack Vectors

  • Model Manipulation: Corrupting or manipulating AI model behavior
  • Data Poisoning: Injecting malicious data into training processes
  • Inference Exploitation: Exploiting model inference to extract information
  • Model Extraction: Unauthorized copying and theft of AI models
  • Adversarial Inputs: Using crafted inputs to manipulate model outputs
  • AI Infrastructure Attacks: Targeting AI-specific infrastructure components

Impact Assessment

  • Model Compromise: Corruption of AI model integrity and performance
  • Data Exposure: Unauthorized access to training data and model parameters
  • Intellectual Property Theft: Theft of proprietary AI models and algorithms
  • System Manipulation: Manipulation of AI system behavior and outputs
  • Privacy Violations: Extraction of sensitive information from AI models
  • Operational Disruption: Disruption of AI-powered services and applications

AI-Specific Vulnerabilities represent emerging threats that target the unique characteristics and vulnerabilities of artificial intelligence systems and models.

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