LLM07:2023 - Inadequate AI Alignment

Description:
Inadequate AI alignment occurs when the LLM’s objectives and behavior do not align with the intended use case, leading to undesired consequences or vulnerabilities.

Common AI Alignment Issues:

• Poorly defined objectives, causing the LLM to prioritize undesired or harmful behaviors.
• Misaligned reward functions or training data, resulting in unintended model behavior.
• Insufficient testing and validation of LLM behavior in various contexts and scenarios.

How to Prevent:

• Clearly define the objectives and intended behavior of the LLM during the design and development process.
• Ensure that the reward functions and training data are aligned with the desired outcomes and do not encourage undesired or harmful behavior.
• Regularly test and validate the LLM’s behavior across a wide range of scenarios, inputs, and contexts to identify and address alignment issues.
• Implement monitoring and feedback mechanisms to continuously evaluate the LLM’s performance and alignment, and update the model as needed to improve alignment.

Example Attack Scenarios: Scenario #1: An LLM trained to optimize for user engagement inadvertently prioritizes controversial or polarizing content, resulting in the spread of misinformation or harmful content.

Scenario #2: An LLM designed to assist with system administration tasks is misaligned, causing it to execute harmful commands or prioritize actions that degrade system performance or security.

By focusing on AI alignment and ensuring that the LLM’s objectives and behavior align with the intended use case, developers can reduce the risk of unintended consequences and vulnerabilities in their LLM implementations.