OMNIGUARD: An Efficient Approach for AI Safety Moderation Across
  Modalities
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.
683df77d4c5b9f381d2f54b9
Sahil Verma
683df77d4c5b9f381d2f54ba
Keegan Hines
683df77d4c5b9f381d2f54bb
Jeff Bilmes
683df77d4c5b9f381d2f54bc
Charlotte Siska
683df77d4c5b9f381d2f54bd
Luke Zettlemoyer
683df77d4c5b9f381d2f54be
Hila Gonen
683df77d4c5b9f381d2f54bf
Chandan Singh
OMNIGUARD: An Efficient Approach for AI Safety Moderation Across
  Modalities
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.
OMNIGUARD detects harmful prompts across languages and modalities by identifying aligned internal representations in large language models, achieving high accuracy and efficiency.
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