AI & ML
Security Testing
Gartner predicts that by 2025, 30% of cyberattacks will leverage AI techniques. As organizations rapidly adopt LLMs and ML systems, new attack vectors emerge. OWASP's Top 10 for LLM Applications identifies critical vulnerabilities specific to AI systems.
AI-Specific Risks
OWASP Top 10 for LLM Applications
The Open Worldwide Application Security Project has identified the most critical security risks to LLM applications. We test all 10 categories.
Prompt Injection
Manipulating LLM via crafted inputs
Insecure Output
Insufficient validation of model outputs
Training Data Poisoning
Compromising training data integrity
Model Denial of Service
Resource exhaustion attacks
Supply Chain
Third-party model and data risks
Sensitive Info Disclosure
Leaking confidential data
Insecure Plugin Design
Vulnerable LLM extensions
Excessive Agency
Overprivileged LLM capabilities
Overreliance
Unchecked dependence on outputs
Model Theft
Unauthorized model extraction
Comprehensive AI Security Testing
Three-pronged approach covering application layer, model integrity, and data pipeline security.
Application Layer
Model Layer
Data Pipeline
Common AI/ML Vulnerabilities
Prompt Leakage
Exposing system prompts, instructions, or internal logic
Data Exfiltration
Extracting training data or sensitive information via queries
Unauthorized Actions
LLM performing privileged operations beyond intended scope
Supply Chain Risks
Compromised models, libraries, or training data sources
AI Systems We Test
LLM Applications
ML Models
AI Infrastructure
Secure Your AI
Before It's Exploited
Specialized testing for emerging AI/ML security threats. Protect your LLM applications, machine learning models, and AI infrastructure from the latest attack techniques.