Prompt Injection Attacks on LLMs: The Hidden AI Phishing Threat

Prompt injection attacks on large language models (LLMs) exploit the trust users place in AI by embedding hidden instructions within the input data, such as invisible HTML, to manipulate the model's behavior. These attacks, which can lead to AI-driven phishing, involve placing malicious prompts in web content or user-provided text, causing models like ChatGPT, Gemini, and Copilot to generate unintended outputs, such as phishing messages. The difficulty in detecting these attacks stems from their lack of traditional malware signatures and their reliance on exploiting the model's attention rather than its code. To mitigate these risks, developers are advised to treat LLM outputs as untrusted input, sanitizing and inspecting HTML, fine-tuning models to ignore hidden text, auditing prompt chains, and using retrieval-augmented generation cautiously. Real-world scenarios illustrate how these attacks can transform AI into a phishing tool, emphasizing the need for robust security measures that focus on context rather than code to maintain trust and verification in AI interactions.