Attack Surface Score for Software Systems

Software attack surfaces define the external boundaries—entry points, communication channels, and sensitive data stores through which adversaries may compromise a system. This paper introduces a scoring mechanism that produces a normalized attack-surface metric in the range of 0–1. Building on the established Damage-Potential-to-Effort ratio, our approach further incorporates real-world vulnerability intelligence drawn from MITRE’s CVE and CWE repositories. We compute each application’s score by ingesting preliminary findings from a static-analysis tool and processing them through our unified model. To assess effectiveness, we validate the scoring system across a spectrum of scenarios, from a simple Java application to complex enterprise applications. The resulting metric offers development and security teams a concise, objective measure to monitor an application’s attack surface and hence proactively identify vulnerabilities in their applications. This tool can also be used to benchmark various third-party or dependent applications, enabling both developers and security practitioners to better manage risk.

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Publication Details

Journal
Future Internet
Published
2025-07-14
DOI
https://doi.org/10.3390/fi17070305
Primary Topic
Information and Cyber Security
Type
article
Field-Weighted Citation Impact
0.00

Funders

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article

Attack Surface Score for Software Systems

Sabina Shrestha, Rokin Maharjan, Yudeep Rajbhandari
Future Internet
Information and Cyber Security
article

Attack Surface Score for Software Systems

Sabina Shrestha, Rokin Maharjan, Yudeep Rajbhandari
article en

Abstract

Software attack surfaces define the external boundaries—entry points, communication channels, and sensitive data stores through which adversaries may compromise a system. This paper introduces a scoring mechanism that produces a normalized attack-surface metric in the range of 0–1. Building on the established Damage-Potential-to-Effort ratio, our approach further incorporates real-world vulnerability intelligence drawn from MITRE’s CVE and CWE repositories. We compute each application’s score by ingesting preliminary findings from a static-analysis tool and processing them through our unified model. To assess effectiveness, we validate the scoring system across a spectrum of scenarios, from a simple Java application to complex enterprise applications. The resulting metric offers development and security teams a concise, objective measure to monitor an application’s attack surface and hence proactively identify vulnerabilities in their applications. This tool can also be used to benchmark various third-party or dependent applications, enabling both developers and security practitioners to better manage risk.

Future InternetVol. 17(7)
East Tennessee State University (US), University of Arizona (US), Baylor University (US)
National Science Foundation
Openalex Percentile: Top 75%
Information and Cyber Security
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