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Language models show unique jailbreak vulnerabilities based on training run

By PulseAugur Editorial · [1 sources] ·

A new research paper from arXiv explores the reproducibility of language model behavior, specifically concerning jailbreaks. The study found that identical pretraining runs of a language model, even with the same data and safety training, exhibit highly correlated responses to jailbreak prompts. However, using different pretraining seeds or data recipes significantly reduces this correlation, suggesting that the specific pretraining run, rather than just the data, leaves a distinct fingerprint on the model's susceptibility to jailbreaks. This implies that current methods for evaluating model safety might not be as robust as assumed, as different training runs can lead to varied vulnerabilities. AI

IMPACT Suggests current safety evaluations may be insufficient due to training run variability.

RANK_REASON Academic paper published on arXiv detailing research findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Language models show unique jailbreak vulnerabilities based on training run

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Academic paper published on arXiv detailing research findings. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chi-Wei Chang, Mu-Tien Kuo, Kuan-Wei Lee, Meng-Hsun Tsai ·

    Identically Trained Language Models Break on Different Jailbreaks

    arXiv:2610.34074v1 Announce Type: cross Abstract: Jailbreak benchmarks test one checkpoint and treat which jailbreaks break it as a property of its pretraining data and safety training. We ask whether another pretraining run of the same data, given the same safety training, would…

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