AI Applications Create a New Attack Surface
The integration of large language models (LLMs) and AI-assisted applications into enterprise systems introduces new security problems that traditional application security has not addressed. Systems that pass user input directly to model processing can become vulnerable to attack vectors such as prompt injection, model manipulation, and sensitive data exfiltration.
OWASP published the LLM Top 10 list to document the risks in this area, classifying critical categories such as prompt injection, insecure output handling, training data poisoning, and model denial of service. Evaluating these risks before or after deploying LLM integrations has now become an organizational security maturity requirement.
The OWASP LLM Application Security Top 10 list published in 2023 has become a de facto reference framework for AI security testing. TUGAY tests cover all risk categories in this list.
AI and LLM Security Testing Scope
TUGAY's AI security tests cover both LLM-layer-specific attack techniques and traditional security vulnerabilities in the integration architecture:
- Prompt injection — direct and indirect attacks
- Jailbreak and system instruction bypass attempts
- Sensitive information and training data exfiltration tests
- RAG (Retrieval Augmented Generation) pipeline security
- Tool calling and function execution security
- Insecure integration of LLM output into the application
- Model API authentication and authorization tests
- Excessive permissions and privilege boundary violations
Test Methodology
- Architecture Analysis: The LLM integration architecture, data flows, tool connections, and permission model are examined.
- Threat Modeling: Possible attack vectors are mapped using the OWASP LLM Top 10 and MITRE ATLAS frameworks.
- Prompt Injection Tests: Direct user input and indirect (through external sources) injection scenarios are applied.
- Data Exfiltration Attempts: Exfiltration of system prompt, training data, and RAG data store content is tested.
- Integration Security: Tools, APIs, and databases connected to the LLM are assessed with traditional security tests.
Reporting and Security Architecture Recommendations
AI security test reports include technical findings and practical remediation recommendations, as well as design principles for secure LLM integration architecture.
- Vulnerability catalogue mapped to OWASP LLM Top 10
- Prompt design and system instruction hardening recommendations
- Input/output sanitization and filtering guide
- Permission minimization and least privilege implementation guide