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Sound Probabilistic Safety Bounds for Large Language Models

Research LLM Safety Evaluation

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Merged summary

TL;DR - This paper introduces a framework for computing formally sound probability bounds on harmful LLM outputs. It enables statistical safety certification, including useful lower bounds when harmful generations are extremely rare.

  • Applies Clopper–Pearson confidence intervals to derive probably approximately correct (PAC) harm-probability bounds.
  • Prioritizes risky branches of the autoregressive generation tree using latent-space features.
  • Formally guarantees that computed lower bounds do not exceed the true harmful-output probability.
  • Demonstrates non-trivial lower bounds for state-of-the-art LLMs.

Sources (1)

Sound Probabilistic Safety Bounds for Large Language Models

arXiv cs.CL Mahdi Nazeri, Anne-Kathrin Schmuck, Sadegh Soudjani, Alessandro Abate 2026-07-22 arXiv:2607.20286
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-08 14:27:27.494543 UTC

TL;DR - This paper introduces a framework for computing formally sound probability bounds on harmful LLM outputs. It enables statistical safety certification, including useful lower bounds when harmful generations are extremely rare.

  • Applies Clopper–Pearson confidence intervals to derive probably approximately correct (PAC) harm-probability bounds.
  • Prioritizes risky branches of the autoregressive generation tree using latent-space features.
  • Formally guarantees that computed lower bounds do not exceed the true harmful-output probability.
  • Demonstrates non-trivial lower bounds for state-of-the-art LLMs.
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