LLM security vulnerabilities: a developer''s checklist

Organizations are rapidly adopting generative AI but often lack formal ethics councils and governance structures, exposing them to LLM-specific risks such as prompt injection, data poisoning, model theft, sensitive-data disclosure, insecure tool use, and shadow AI, with potentially severe regulatory and financial consequences under frameworks such as HIPAA and GDPR. Effective security requires layered controls across the AI lifecycle, including input and output guardrails, least-privilege permissions, tool allowlists, encrypted credential storage, sandboxing, OAuth and SSO authentication, role-based access control, session isolation, continuous monitoring, comprehensive audit logs, PII redaction, and protection of files such as environment configurations and SSH keys. The material recommends aligning practices with the OWASP Top 10 for LLMs, NIST AI Risk Management Framework, and applicable compliance standards; incorporating threat modeling, dependency scanning, security reviews, CI/CD checks, developer training, regular red-teaming, incident-response planning, and resilient production infrastructure. MintMCP Gateway and its LLM Proxy are presented as tools for centralizing MCP server governance, authentication, access controls, monitoring, audit trails, sensitive-file protections, and real-time blocking of risky tool calls, helping organizations convert local AI integrations into more secure enterprise services.

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