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Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversations

About

We introduce Llama Guard 3 Vision, a multimodal LLM-based safeguard for human-AI conversations that involves image understanding: it can be used to safeguard content for both multimodal LLM inputs (prompt classification) and outputs (response classification). Unlike the previous text-only Llama Guard versions (Inan et al., 2023; Llama Team, 2024b,a), it is specifically designed to support image reasoning use cases and is optimized to detect harmful multimodal (text and image) prompts and text responses to these prompts. Llama Guard 3 Vision is fine-tuned on Llama 3.2-Vision and demonstrates strong performance on the internal benchmarks using the MLCommons taxonomy. We also test its robustness against adversarial attacks. We believe that Llama Guard 3 Vision serves as a good starting point to build more capable and robust content moderation tools for human-AI conversation with multimodal capabilities.

Jianfeng Chi, Ujjwal Karn, Hongyuan Zhan, Eric Smith, Javier Rando, Yiming Zhang, Kate Plawiak, Zacharie Delpierre Coudert, Kartikeya Upasani, Mahesh Pasupuleti• 2024

Related benchmarks

TaskDatasetResultRank
Response Harmfulness DetectionXSTEST-RESP
Response Harmfulness F189.8
34
Safety ClassificationSafeRLHF
F1 Score0.4331
32
Response Harmfulness ClassificationWildGuard (test)
F1 (Total)66.39
30
Safety EvaluationUnsafeBench
F1 Score0.00e+0
24
Response Harmfulness DetectionHarmBench
F1 Score82.92
23
Response ClassificationBeaverTails V Text-Image Response
F1 Score70.91
23
Prompt Harmfulness DetectionText & Image Benchmarks Average
F1 Score51.05
19
Response Harmfulness DetectionBeavertails
F1 Score69.51
18
Response ClassificationWild Guard Text Response
F1 Score87.19
16
Response ClassificationXSTest Text Response
F1 Score94.96
16
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