// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Model Security
Protecting AI models from attacks, misuse, and unauthorized access.
TECHNICAL DEFINITION
The discipline focused on safeguarding AI models against various threats, including adversarial attacks (e.g., evasion, poisoning), intellectual property theft (e.g., model extraction), and ensuring their integrity, confidentiality, and availability throughout their lifecycle.
BACKGROUND
Prompt injection is a cybersecurity exploit and an attack vector in which innocuous-looking inputs are designed to cause unintended behavior in machine learning models, particularly large language models (LLMs). The attack takes advantage of the model's inability to distinguish between developer-defined prompts and user inputs to bypass safeguards and influence model behaviour. While LLMs are designed to follow trusted instructions, they can be manipulated into carrying out unintended responses through carefully crafted inputs.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- AI security
- robust AI
- trustworthy AI
- AI safety
USAGE NOTE
Model security is paramount for deploying reliable and safe AI systems in critical applications.
DEVELOPERS
Organizations developing technology related to Model Security.
Develops comprehensive tools and practices for Responsible AI, MLOps security, and adversarial robustness, focusing on securing AI models throughout their lifecycle against various threats, including those from prompt manipulation.
Invests in secure and responsible AI development, offering platforms and research on model robustness, defenses against adversarial attacks, and interpretability to ensure the security of AI systems against malicious inputs.
Conducts extensive research and develops solutions for AI trustworthiness, including robustness against adversarial attacks, fairness, and explainability, all critical for securing AI models and ensuring their integrity.
Specializes in AI security, offering solutions to detect and defend against adversarial attacks on AI models, thereby protecting model integrity and preventing manipulation through malicious inputs.
Provides an AI firewall and MLOps security platform designed to prevent AI failures, secure models from adversarial attacks, and ensure robust and reliable model behavior in production environments.
Heavily invests in AI safety and security research, focusing on preventing misuse, ensuring model alignment, and mitigating vulnerabilities related to prompt engineering, such as prompt injection and data exfiltration.
Dedicated to AI safety and interpretability, developing techniques and models like 'Constitutional AI' to make large language models more robust, steerable, and secure against various forms of adversarial prompting and misuse.
A cybersecurity research and consulting firm that audits and develops tools to identify and mitigate security vulnerabilities in AI/ML systems, including issues related to model integrity, data privacy, and adversarial robustness.