In all three cases, the core issue is a prompt injection vulnerability. An attacker can craft input to the LLM that leads to the LLM using attacker-supplied input as its core instruction set, and not the original prompt. This enables the user to manipulate the LLM response returned to the ...
Prompt injection manipulates models with direct injections that overwrite system prompts or indirect injections that manipulate user inputs. Insecure output handling exposes backend web systems to malicious code that’s inserted into front-end applications with the hopes of tricking end-users into clicking...
attempting to make the LLM produce a specific response. An example of a direct prompt injection leading to remote code execution is shown in Figure 1. For more details about direct prompt injection, seeSecuring LLM Systems Against Prompt Injection. ...
Verify that systems properly handle queries that may give rise to inappropriate, malicious, or illegal usage, including facilitating manipulation, extortion, targeted impersonation, cyber-attacks, and weapons creation: \n - Prompt injection (OWASP LLM01) \n - Insecure Outpu...
Prompt injection manipulates models with direct injections that overwrite system prompts or indirect injections that manipulate user inputs. Insecure output handling exposes backend web systems to malicious code that’s inserted into front-end applications with the hopes of tricking end-users into clicking...
The in-context learning examples used to prompt the Large Language Models (LLMs) were manually crafted and validated against a vulnerable application connected to the target database. These examples served as a foundation for the LLMs to generate obfuscated SQL injection (SQLi) samples. The exampl...
即时注入是一种新的攻击技术,专门针对大语言模型 (LLMs),使得攻击者能够操纵 LLM 的输出。由于 LLM 越来越多地配备了“插件”,通过访问最新信息、执行复杂的计算以及通过其提供的 API 调用外部服务来更好地响应用户请求,这种攻击变得更加危险。即时注入攻击不仅欺骗 LLM ,而且可以利用其对插件的使用来实现其目标。