Evidence map›Paper›PMID 40545447›Full record

ArticleRisk analysis : an official publication of the Society for Risk Analysis2025

Risks of ignoring uncertainty propagation in AI-augmented security pipelines.

Emanuele Mezzi, Aurora Papotti, Fabio Massacci, Katja Tuma

Abstract read
In one paragraph

Article in Risk analysis : an official publication of the Society for Risk Analysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Risks of ignoring uncertainty propagation in AI-augmented security pipelines.Risk analysis : an official publication of the Society for Risk Analysis · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Emanuele MezziDepartment of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID https://orcid.org/0009-0001-9007-8260
Aurora PapottiDepartment of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID https://orcid.org/0000-0003-3207-7662
Fabio MassacciDepartment of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.ORCID https://orcid.org/0000-0002-1091-8486
Katja TumaDepartment of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.ORCID https://orcid.org/0000-0001-7189-2817

Funding

HORIZON EUROPE Civil security for society 101120393Nederlandse Organisatie voor Wetenschappelijk Onderzoek KIC1.VE01.20.004
6 · The paper itself

Abstract

The use of AI technologies is being integrated into the secure development of software-based systems, with an increasing trend of composing AI-based subsystems (with uncertain levels of performance) into automated pipelines. This presents a fundamental research challenge and seriously threatens safety-critical domains. Despite the existing knowledge about uncertainty in risk analysis, no previous work has estimated the uncertainty of AI-augmented systems given the propagation of errors in the pipeline. We provide the formal underpinnings for capturing uncertainty propagation, develop a simulator to quantify uncertainty, and evaluate the simulation of propagating errors with one case study. We discuss the generalizability of our approach and its limitations and present recommendations for evaluation policies concerning AI systems. Future work includes extending the approach by relaxing the remaining assumptions and by experimenting with a real system.

Indexed as

artificial intelligenceautomatic program repairuncertainty quantification

Identifiers

PMID40545447
PMCPMC12747714

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.