AI Security Vulnerabilities
Security vulnerabilities in AI systems represent critical weaknesses that can be exploited by malicious actors to compromise system integrity, extract sensitive information, or manipulate model behavior for harmful purposes.
Understanding Security Vulnerabilities
Security vulnerabilities differ from business failures in that they focus on malicious exploitation and system integrity rather than accuracy and reliability. These vulnerabilities can lead to data breaches, privacy violations, system manipulation, and other security incidents that pose significant risks to organizations and users.
Tip
You can find examples of security vulnerabilities in our RealHarm dataset.
Types of Security Vulnerabilities
A security vulnerability where malicious input manipulates the model’s behavior or extracts sensitive information.
Production of violent, illegal, or inappropriate material by AI models.
Revealing internal system details, training data, or confidential information.
Manipulation of response structure for malicious purposes or poor output formatting.
Vulnerability to adversarial inputs or edge cases causing inconsistent behavior.
Biased responses that perpetuate harmful stereotypes and discriminatory behavior.
Getting Started with Security Testing
To begin securing your AI systems:
Our state-of-the-art enterprise-grade security vulnerability testing.
Our open-source toolkit for security vulnerability testing.