AI Vulnerability Types
Comprehensive guide to AI and machine learning vulnerability types. Understand threats, attack vectors, and defense strategies.
LLM & GenAI Vulnerabilities
Malicious inputs that manipulate LLM behavior by injecting instructions into prompts, bypassing safety controls.
Learn MoreInjection of malicious data into training sets to compromise model behavior or create backdoors.
Learn MoreStealing model parameters, architecture, or training data through API queries and analysis.
Learn MoreInsufficient validation of LLM outputs leading to XSS, SQL injection, or other downstream vulnerabilities.
Learn MoreModel Security Vulnerabilities
Carefully crafted inputs designed to fool models into making incorrect predictions or classifications.
Learn MoreReconstructing training data or sensitive information from model outputs and parameters.
Learn MoreDetermining whether specific data was used in model training, potentially exposing sensitive information.
Learn MoreHidden triggers embedded in models that cause malicious behavior when activated by specific inputs.
Learn MoreAgent & System Vulnerabilities
Exploiting autonomous agents to perform unauthorized actions or bypass security policies.
Learn MoreCompromising AI systems through malicious dependencies, pre-trained models, or datasets.
Learn MoreVulnerabilities in LLM plugins and extensions that allow unauthorized access or code execution.
Learn MoreAI systems granted excessive permissions or autonomy leading to unintended or harmful actions.
Learn MoreLearn More About AI Security
Explore our comprehensive resources on AI vulnerabilities and security best practices.