Unmasking the Canvas: A Dynamic Benchmark for Image Generation Jailbreaking and LLM Content Safety

TL;DR

This paper introduces a dynamic benchmark dataset called UTCB to evaluate the vulnerability of large language models in generating unsafe images through prompt-based jailbreaks, highlighting the need for improved content safety measures.

Contribution

The paper presents a scalable, evolving benchmark dataset with structured prompt strategies and multi-tiered verification to assess and improve LLM content safety in image generation.

Findings

Prompt engineering can induce unsafe image generation.

Multilingual obfuscation challenges LLM safety defenses.

The benchmark supports automated and manual verification tiers.

Abstract

Existing large language models (LLMs) are advancing rapidly and produce outstanding results in image generation tasks, yet their content safety checks remain vulnerable to prompt-based jailbreaks. Through preliminary testing on platforms such as ChatGPT, MetaAI, and Grok, we observed that even short, natural prompts could lead to the generation of compromising images ranging from realistic depictions of forged documents to manipulated images of public figures. We introduce Unmasking the Canvas (UTC Benchmark; UTCB), a dynamic and scalable benchmark dataset to evaluate LLM vulnerability in image generation. Our methodology combines structured prompt engineering, multilingual obfuscation (e.g., Zulu, Gaelic, Base64), and evaluation using Groq-hosted LLaMA-3. The pipeline supports both zero-shot and fallback prompting strategies, risk scoring, and automated tagging. All generations are stored with rich metadata and curated into Bronze (non-verified), Silver (LLM-aided verification), and Gold (manually verified) tiers. UTCB is designed to evolve over time with new data sources, prompt templates, and model behaviors. Warning: This paper includes visual examples of adversarial inputs designed to test model safety. All outputs have been redacted to ensure responsible disclosure.