AI reliability, security, alignment, misuse prevention and responsible development.
The effort to ensure an AI system’s behavior and objectives reliably match intended human goals and constraints.
Systematic skew in AI outputs resulting from flawed data, design, or deployment choices.
Safety mechanisms and policies put in place to ensure AI systems operate within defined ethical, legal, and operational boundaries.
The practice of crafting specific prompts to bypass an AI model's built-in safety restrictions and moderation filters.
Structured adversarial testing intended to uncover unsafe behavior, security weaknesses, and failure modes in AI systems.
A security vulnerability where malicious instructions disguised as normal input manipulate an AI model's intended behavior.