title: OMNIGUARD: An Efficient Approach for AI Safety Moderation Across
    Modalities
summary: The emerging capabilities of large language models (LLMs) have sparked
  concerns about their immediate potential for harmful misuse. The core approach
  to mitigate these concerns is the detection of harmful queries to the model.
  Current detection approaches are fallible, and are particularly susceptible to
  attacks that exploit mismatched generalization of model capabilities (e.g.,
  prompts in low-resource languages or prompts provided in non-text modalities
  such as image and audio). To tackle this challenge, we propose OMNIGUARD, an
  approach for detecting harmful prompts across languages and modalities. Our
  approach (i) identifies internal representations of an LLM/MLLM that are
  aligned across languages or modalities and then (ii) uses them to build a
  language-agnostic or modality-agnostic classifier for detecting harmful
  prompts. OMNIGUARD improves harmful prompt classification accuracy by 11.57\%
  over the strongest baseline in a multilingual setting, by 20.44\% for
  image-based prompts, and sets a new SOTA for audio-based prompts. By
  repurposing embeddings computed during generation, OMNIGUARD is also very
  efficient (approx 120 times faster than the next fastest baseline). Code
  and data are available at: https://github.com/vsahil/OmniGuard.
paper_authors._id: 683df77d4c5b9f381d2f54b9
paper_authors.name: Sahil Verma
paper_authors._id: 683df77d4c5b9f381d2f54ba
paper_authors.name: Keegan Hines
paper_authors._id: 683df77d4c5b9f381d2f54bb
paper_authors.name: Jeff Bilmes
paper_authors._id: 683df77d4c5b9f381d2f54bc
paper_authors.name: Charlotte Siska
paper_authors._id: 683df77d4c5b9f381d2f54bd
paper_authors.name: Luke Zettlemoyer
paper_authors._id: 683df77d4c5b9f381d2f54be
paper_authors.name: Hila Gonen
paper_authors._id: 683df77d4c5b9f381d2f54bf
paper_authors.name: Chandan Singh
paper_title: OMNIGUARD: An Efficient Approach for AI Safety Moderation Across
    Modalities
paper_summary: The emerging capabilities of large language models (LLMs) have sparked
  concerns about their immediate potential for harmful misuse. The core approach
  to mitigate these concerns is the detection of harmful queries to the model.
  Current detection approaches are fallible, and are particularly susceptible to
  attacks that exploit mismatched generalization of model capabilities (e.g.,
  prompts in low-resource languages or prompts provided in non-text modalities
  such as image and audio). To tackle this challenge, we propose OMNIGUARD, an
  approach for detecting harmful prompts across languages and modalities. Our
  approach (i) identifies internal representations of an LLM/MLLM that are
  aligned across languages or modalities and then (ii) uses them to build a
  language-agnostic or modality-agnostic classifier for detecting harmful
  prompts. OMNIGUARD improves harmful prompt classification accuracy by 11.57\%
  over the strongest baseline in a multilingual setting, by 20.44\% for
  image-based prompts, and sets a new SOTA for audio-based prompts. By
  repurposing embeddings computed during generation, OMNIGUARD is also very
  efficient (approx 120 times faster than the next fastest baseline). Code
  and data are available at: https://github.com/vsahil/OmniGuard.
paper_ai_summary: OMNIGUARD detects harmful prompts across languages and modalities by identifying aligned internal representations in large language models, achieving high accuracy and efficiency.
paper_ai_keywords: large language models LLMs MLLMs harmful queries OMNIGUARD language-agnostic classifier modality-agnostic classifier harmful prompts multilingual setting image-based prompts audio-based prompts embeddings generation