AI Attack Vectors
Comprehensive analysis of attack vectors targeting AI systems, from adversarial ML to supply chain threats
Attacks that manipulate ML models through carefully crafted inputs to cause misclassification
Common Examples:
Techniques to replicate or steal proprietary AI models through query-based attacks
Common Examples:
Compromising AI systems through malicious dependencies, datasets, or pre-trained models
Common Examples:
Extracting sensitive training data or membership information from AI models
Common Examples:
Embedding hidden malicious behaviors in AI models that activate under specific conditions
Common Examples:
Techniques to bypass AI-based security systems and detection mechanisms
Common Examples:
Exploiting distributed learning systems through malicious participants or poisoned updates
Common Examples:
Extracting information from AI systems through physical characteristics and timing
Common Examples:
Attacks targeting AI systems through network infrastructure and communication channels
Common Examples:
Direct attacks on AI applications, APIs, and software implementations
Common Examples:
Human-targeted attacks exploiting trust and social dynamics in AI systems
Common Examples:
Direct physical access attacks on AI hardware and infrastructure