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ARMS: A Vision for Actor Reputation Metric Systems in the Open-Source Software Supply Chain

Kelechi G. Kalu, Sofia Okorafor, Betül Durak, Kim Laine, Radames C. Moreno, Santiago Torres-Arias, James C. Davis

TL;DR

This vision paper advocates ARMS, an Actor Reputation Metric System, to enhance OSS supply chain security by evaluating a contributor's cybersecurity posture through a set of predefined signals derived from standards and tools. It argues that artifact-centric checks alone are insufficient and proposes a three-part trust model (trustor, trustee, trust engine) with a seven-signal framework and weighting scheme to compute a composite reputation score. The authors outline design-of-experiments, including quasi-experimental and user-behavior studies, and illustrate the approach with worked examples (XZ Utils, Dexcom, ESLint) to demonstrate potential incentives, limitations, and real-world implications. They also discuss threats to validity, privacy concerns, and future work on ecosystem heterogeneity, intent inference, and privacy-preserving mechanisms for practical deployment.

Abstract

Many critical information technology and cyber-physical systems rely on a supply chain of open-source software projects. OSS project maintainers often integrate contributions from external actors. While maintainers can assess the correctness of a change request, assessing a change request's cybersecurity implications is challenging. To help maintainers make this decision, we propose that the open-source ecosystem should incorporate Actor Reputation Metrics (ARMS). This capability would enable OSS maintainers to assess a prospective contributor's cybersecurity reputation. To support the future instantiation of ARMS, we identify seven generic security signals from industry standards; map concrete metrics from prior work and available security tools, describe study designs to refine and assess the utility of ARMS, and finally weigh its pros and cons.

ARMS: A Vision for Actor Reputation Metric Systems in the Open-Source Software Supply Chain

TL;DR

This vision paper advocates ARMS, an Actor Reputation Metric System, to enhance OSS supply chain security by evaluating a contributor's cybersecurity posture through a set of predefined signals derived from standards and tools. It argues that artifact-centric checks alone are insufficient and proposes a three-part trust model (trustor, trustee, trust engine) with a seven-signal framework and weighting scheme to compute a composite reputation score. The authors outline design-of-experiments, including quasi-experimental and user-behavior studies, and illustrate the approach with worked examples (XZ Utils, Dexcom, ESLint) to demonstrate potential incentives, limitations, and real-world implications. They also discuss threats to validity, privacy concerns, and future work on ecosystem heterogeneity, intent inference, and privacy-preserving mechanisms for practical deployment.

Abstract

Many critical information technology and cyber-physical systems rely on a supply chain of open-source software projects. OSS project maintainers often integrate contributions from external actors. While maintainers can assess the correctness of a change request, assessing a change request's cybersecurity implications is challenging. To help maintainers make this decision, we propose that the open-source ecosystem should incorporate Actor Reputation Metrics (ARMS). This capability would enable OSS maintainers to assess a prospective contributor's cybersecurity reputation. To support the future instantiation of ARMS, we identify seven generic security signals from industry standards; map concrete metrics from prior work and available security tools, describe study designs to refine and assess the utility of ARMS, and finally weigh its pros and cons.

Paper Structure

This paper contains 31 sections, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. The Open-Source Software Supply Chain
  4. Artifact-Based Evaluations Are Not Enough
  5. Using Actor Reputation to Establish Trust
  6. Threat Model
  7. Threat Actors
  8. Examples
  9. Dexcom (Inadvertent Vulnerability)
  10. XZ Utils Backdoor (Reputation Spoofing)
  11. ESLint Credential Compromise (Impersonation)
  12. ARMS Conceptual Model
  13. System Overview
  14. Interaction Data -- Security Signals and Metrics Definitions
  15. Trust Engine -- Reputation Computation
  16. ...and 16 more sections

Figures (1)

  • Figure 1: Overview of proposed ARMS system and context case study. Potential contributors (trustees), who may be malicious (Actor A), inadequately expertised (Actor B), or genuine (and capable) (Actor C), express interest or submit change requests . The maintainer team (trustors) requests reputation information on these contributors. The ARMS system retrieves each contributor’s interaction history and quantifies it using the defined security signals and metrics (Interaction data formatter & security signal scoring). Next, the reputation calculator weights these signal values by package usage, community tenure, and centrality, then composites the results and compares them to ecosystem-wide benchmarks (Impact & Benchmark Scoring). Finally, each trustee’s reputation score and recommended action are provided to the maintainer team.