EUVD-2006-0565

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EUVD-2008-1616

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EUVD-2020-25203

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EUVD-2017-12176

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EUVD-2021-16681

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EUVD-2021-15350

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EUVD-2012-0854

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EUVD-2003-0949

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EUVD-2018-1191

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EUVD-2019-15100

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EUVD-2020-6307

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EUVD-2004-0446

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EUVD-2021-19544

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EUVD-2005-0138

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EUVD-2019-16853

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EUVD-2012-0742

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EUVD-2020-16975

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EUVD-2018-6659

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EUVD-2004-0138

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Towards Reliable and Practical LLM Security Evaluations Via Bayesian Modelling

Before adopting a new large language model LLM architecture, it is critical to understand vulnerabilities accurately. Existing evaluations can be difficult to trust, often drawing conclusions from LLMs that are not meaningfully comparable, relying on heuristic inputs or employing metrics that fai...