Safeguarding Privacy and Mitigating Vulnerabilities: Navigating Security Challenges in Generative AI

LLM outputs are inconsistent and can vary significantly even for the same prompt asked differently - verification of outputs is critical

System prompts act as guardrails and need protection since gaining access to them allows manipulation of the LLM’s behavior

Never use LLM outputs directly for SQL queries, terminal commands, or critical operations without validation

Data leakage is a major concern - sensitive information sent to LLMs can be exposed through training data or system vulnerabilities

Prompt injection and jailbreaking techniques can trick LLMs into bypassing safety controls

Code interpreters and plugins need careful security review as they can enable remote code execution

Agent-based systems can be exploited through recursive prompts that drain resources or expose vulnerabilities

Image generation models can have biases and security issues stemming from training data

Implement specific templates and validation steps for both input prompts and output responses

Use sandboxing and rate limiting to prevent abuse of LLM systems