Neural Networks Found Inherently Vulnerable to Generating Any Output

A new paper published on arXiv explores the surjectivity of neural networks, a property that indicates whether any output can be generated from some input. The research demonstrates that many common neural network architectures, including GPT-style transformers and diffusion models, are almost always surjective. This finding suggests an inherent vulnerability in these models, as it implies they can be prompted to generate any output, including harmful content, raising significant safety and jailbreak concerns. AI

IMPACT Reveals inherent surjectivity in common generative models, suggesting unavoidable vulnerabilities to generating harmful content.

RANK_REASON Academic paper published on arXiv detailing a new finding about neural network properties. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• generative pre-trained transformer
• Haozhe Jiang
• Hugging Face

• paper
• safety

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