Output Scouting is a systematic methodology for identifying potentially harmful responses from Large Language Models before they reach production environments.

• Addresses the critical challenge that even well-trained LLMs have non-zero probabilities of generating harmful outputs
• Uses a strategic sampling approach to find outputs with specific harmful characteristics
• Demonstrates effectiveness through real-world testing across multiple safety-critical scenarios
• Provides security professionals with a practical framework for proactive LLM risk assessment

This research is essential for organizations deploying LLMs, offering a structured approach to identify and mitigate security vulnerabilities before they impact users or create legal liability.

Output Scouting: Auditing Large Language Models for Catastrophic Responses