This research introduces the Red Team Diffuser (RTD) framework to identify critical security flaws in large Vision-Language Models by generating images that can bypass safety guardrails.

• Demonstrates that current VLM alignment mechanisms fail to address risks from toxic text continuation tasks
• Reveals vulnerabilities across multiple state-of-the-art VLMs including GPT-4V and Claude 3
• Provides a systematic approach for security teams to identify and mitigate safety risks
• Shows how generated images can induce harmful outputs despite no explicit harmful instructions

This work matters for security professionals by exposing alignment deficiencies in widely deployed AI systems and proposing more robust defense mechanisms against evolving threats.

Reinforced Diffuser for Red Teaming Large Vision-Language Models