Large Language Models (LLMs) are vulnerable to various forms of attacks, including model chaining prompt injection, where attackers craft a sequence of seemingly benign prompts that collectively lead to the execution of malicious code. By exploiting the LLM’s sequential prompt processing, attackers can manipulate the model into performing unintended actions, highlighting the importance of robust input validation mechanisms.
Additionally, LLMs can be compromised through poisoned training data containing prompts embedded with malicious code. Attackers leverage this vulnerability to train the model to associate innocuous inputs with harmful actions, leading to the execution of malicious code when the model encounters similar prompts in the future. To mitigate these risks, developers must implement stringent security measures to detect and prevent the injection of malicious prompts during model training and deployment phases, ensuring the integrity and trustworthiness of LLM-based systems.
