Most enterprises are integrating LLMs into products and internal processes without an appropriate security assessment framework. With experience auditing dozens of LLM integrations, DNA has developed a 5-pillar framework to help CTOs and CISOs comprehensively assess risks.
5 Pillars of LLM Security Assessment
- API Security: API key management, rate limiting, authentication, and access control
- Data Protection: Input/output data control, PII filtering, and data retention policies
- Model Security: Prompt injection resistance, jailbreak testing, and output safety
- Infrastructure: Network segmentation, logging, monitoring, and incident response
- Compliance: GDPR, Vietnam PDPL, industry-specific regulations, and AI governance
Most Common Risks
API Key Exposure
In 70% of audits, DNA discovered API keys hardcoded in frontend code, committed in Git history, or stored insecurely. The cost of API key leaks can reach hundreds of thousands of USD when attackers use keys to run inference.
# Common API key exposure patterns found in audits
# 1. Hardcoded in frontend (React/Next.js)
const OPENAI_KEY = "sk-proj-abc123..." # Exposed!
# 2. In .env committed to git
# .env (should be in .gitignore)
ANTHROPIC_API_KEY=sk-ant-abc123...
# 3. Recommended: Use server-side proxy
# /api/chat route handler
async function handler(req) {
// Key only exists server-side
const key = process.env.ANTHROPIC_API_KEY;
// Validate & sanitize user input first
const response = await anthropic.complete(req.body);
return sanitizeOutput(response);
}
Real Audit Results
Across 25 LLM integration audit projects in 2025-2026, DNA recorded an average of 12 findings per engagement, with 3-4 findings at Critical or High level. Most common were missing input validation, API key exposure, and missing rate limiting.
info DNA provides a complete LLM Security Assessment package in 2-3 weeks, including penetration testing, code review, and executive report with detailed remediation roadmap.
Securing LLM integrations is not about adding another layer - it's about rethinking the entire trust boundary when a reasoning entity sits between user and data.