The article addresses vulnerabilities in large language models (LLMs) such as prompt injection and stealing. It explains these attack vectors, emphasizing that attackers may seek to access business data, gain personal advantage, or exploit tools. The piece recommends several defense strategies: creating public-facing system prompts, embedding protective instructions, employing adversarial detectors to identify malicious prompts, and fine-tuning models for enhanced security. Each method has its benefits and limitations, and understanding them is crucial for securing LLM-based systems against potential breaches and misuse.
Your LLM-based systems are at risk of being attacked to access business data, gain personal advantage, or exploit tools to the same ends.
Everything you put in the system prompt is public data. Consider it as being public. Don't even try to hide it. People will find out about it.
To defend against prompt injections and prompt stealing, add instructions in your prompt for a base layer of security.
Add adversarial detectors as a second layer of security to determine if a prompt actually is malicious or not before allowing it into your system.
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