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Life sciences · Preprint
arXiv · August 11, 2026
Posted before peer review. The findings may change or fail to hold.
ProbGuard is a proposed probabilistic guardrail architecture that estimates safety risk from LLM output distributions using early decoding signals and Monte-Carlo sampling. The method achieved improved calibration metrics (Brier score and ECE reduction) and low jailbreak success rates in algorithmic benchmarks, but remains an unrefereed computational contribution without independent validation or clinical deployment evidence.
Algorithmic method development with empirical evaluation on benchmark datasets. Large language model outputs and generated text; evaluated against jailbreak attack scenarios; no human subjects or clinical populations enrolled.. Intervention: ProbGuard: a probabilistic, architecture-agnostic guardrail that estimates safety probability from early LLM output distributional signals and enables early stopping of unsafe outputs.. Compared with: Existing guardrails (baselines unspecified by name in abstract); deterministic classification approaches to safety assessment..
Achieved 79.6% reduction in average Brier score over best baseline across nine model–dataset combinations Achieved 71.9% reduction in average ECE over best baseline Limited attack success rate to at most 1% across six representative jailbreak attacks after observing first ten decoding steps
Calibration metrics (Brier score, ECE) are proxy measures; no evaluation of real-world safety outcomes or user harm
This is a computational methods paper not directly applicable to clinical practice. It may inform future development of LLM safety systems in research settings, but has not been validated for deployment, regulatory compliance, or clinical use and is not peer reviewed.
This is an unrefereed preprint describing a novel computational method for LLM safety assessment; it has not undergone peer review and the claims rest on algorithmic performance metrics rather than clinical or validated real-world outcomes.
As stated by the source record.
Quoted from the source exactly as published.
This is a computational methods paper not directly applicable to clinical practice. It may inform future development of LLM safety systems in research settings, but has not been validated for deployment, regulatory compliance, or clinical use and is not peer reviewed.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Recent research on Large Language Model (LLM) safety has widely adopted guardrails to identify unsafe LLM outputs. Existing guardrails typically formulate safety assessment as a deterministic classification task, mapping a discrete token sequence to a discrete safety label. However, this paradigm has two limitations: First, safety assessment is inherently an uncertain problem, particularly during the early generation state. Second, relying solely on discrete token sequences discards the rich probabilistic information embedded in the LLM output distribution. To address these limitations, we propose the first completely probabilistic architecture-agnostic guardrail \textsc{ProbGuard} to leverage the LLM early output distributional signals for estimating and calibrating the safety probability, thereby enabling early stopping of unsafe ongoing outputs. Specifically, given an LLM's generated prefix distribution, we formulate the safety risk as the unsafe probability of its continued generation dynamics and estimate this risk by Monte-Carlo sampling. Through post-training on the distributional signals and calibrated safety risk, \textsc{ProbGuard} achieves the best calibration performance across all nine model--dataset combination settings, reducing the average Brier score and ECE by 79.6\% and 71.9\%, respectively, over the best baseline. \textsc{ProbGuard} further limits the attack success rate to at most 1\% across six representative jailbreak attacks after observing the LLM early output distributions from only the first ten decoding steps.
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