8.5Understanding Each Security Layer

SAST (Static Application Security Testing)

Analyzes source code without executing it. Catches code-level vulnerabilities early.

What it catches in AI apps:

• LLM output used in SQL/shell without sanitization
• API keys hardcoded in prompt templates
• Missing output validation on LLM responses
• Insecure deserialization of model outputs

Limitations: Cannot detect runtime behavior, configuration issues, or logical flaws.

DAST (Dynamic Application Security Testing)

Tests the running application by sending requests and analyzing responses.

What it catches in AI apps:

• XSS via LLM-generated HTML content
• SSRF through AI URL fetching features
• Authentication bypass on AI endpoints
• Information disclosure in error messages

Limitations: Requires a running environment, slower than SAST, cannot see code-level issues.

SCA (Software Composition Analysis)

Scans dependencies for known vulnerabilities (CVEs).

What it catches in AI apps:

• CVEs in PyTorch, TensorFlow, LangChain, Hugging Face libraries
• Vulnerable transitive dependencies in the ML pipeline
• License compliance issues with model dependencies

ML-specific SCA concerns:

• ML libraries are updated less frequently than web frameworks
• Model files from external sources may contain malicious payloads
• The LangChain ecosystem has had several critical CVEs