This research explores the paradoxical phenomenon where Vision Large Language Models (VLLMs) are both easily attacked and easily defended, revealing critical insights for security practitioners.

• Dual High Performance: Both jailbreak attacks and defensive mechanisms against VLLMs achieve high success rates with minimal effort
• Over-Prudence Problem: Current defenses often reject harmless inputs, revealing an important trade-off between safety and utility
• Benchmark Limitations: Existing evaluation frameworks fail to adequately measure the true robustness of defense mechanisms
• Novel Solution: The proposed LLM-Pipeline approach offers a more balanced safety-aware method to improve VLLM trustworthiness

This research matters because it challenges conventional security assessment approaches for VLLMs and provides a framework for developing more reliable defense mechanisms that maintain model utility in real-world applications.

The VLLM Safety Paradox: Dual Ease in Jailbreak Attack and Defense