A company is testing the security of a foundation model (FM). During testing, the company wants to get around the safety features and make harmful content.
Jailbreaking refers to bypassing or disabling the security restrictions placed on a system—in this case, a foundation model (FM)—to make the system behave in unintended ways, often to produce harmful or malicious content. In the context of AI, jailbreaking typically involves manipulating the model's behavior or output by exploiting vulnerabilities in its design or safety features.
D. Jailbreak
Explanation:
Jailbreaking is a technique used to bypass the safety features and restrictions of a foundation model (FM). The goal is to manipulate the model into generating harmful, inappropriate, or otherwise unintended content, despite the safeguards in place. This is often done to test the robustness of the model's safety mechanisms.
ML Jailbreak security
ML jailbreak refers to techniques used to bypass the safety and security measures of machine learning models, particularly large language models (LLMs). This can lead to the model producing harmful, inappropriate, or unintended content1. Here are some key points about ML jailbreak security
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