Towards Socio-Technical Topology-Aware Adaptive Threat Detection in Software Supply Chains
- URL: http://arxiv.org/abs/2510.21452v1
- Date: Fri, 24 Oct 2025 13:30:10 GMT
- Title: Towards Socio-Technical Topology-Aware Adaptive Threat Detection in Software Supply Chains
- Authors: Thomas Welsh, Kristófer Finnsson, Brynjólfur Stefánsson, Helmut Neukirchen,
- Abstract summary: Software supply chains (SSCs) are complex systems composed of dynamic, heterogeneous technical and social components.<n>Attacks on SSCs are increasing, yet pervasive vulnerability analysis is challenging due to their complexity.<n>We identify challenges and research directions to achieve this vision considering techniques for developer and software analysis, decentralised adaptation and the need for a test bed for software supply chain security research.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Software supply chains (SSCs) are complex systems composed of dynamic, heterogeneous technical and social components which collectively achieve the production and maintenance of software artefacts. Attacks on SSCs are increasing, yet pervasive vulnerability analysis is challenging due to their complexity. Therefore, threat detection must be targeted, to account for the large and dynamic structure, and adaptive, to account for its change and diversity. While current work focuses on technical approaches for monitoring supply chain dependencies and establishing component controls, approaches which inform threat detection through understanding the socio-technical dynamics are lacking. We outline a position and research vision to develop and investigate the use of socio-technical models to support adaptive threat detection of SSCs. We motivate this approach through an analysis of the XZ Utils attack whereby malicious actors undermined the maintainers' trust via the project's GitHub and mailing lists. We highlight that monitoring technical and social data can identify trends which indicate suspicious behaviour to then inform targeted and intensive vulnerability assessment. We identify challenges and research directions to achieve this vision considering techniques for developer and software analysis, decentralised adaptation and the need for a test bed for software supply chain security research.
Related papers
- ORCA -- An Automated Threat Analysis Pipeline for O-RAN Continuous Development [57.61878484176942]
Open-Radio Access Network (O-RAN) integrates numerous software components in a cloud-like deployment, opening the radio access network to previously unconsidered security threats.<n>Current vulnerability assessment practices often rely on manual, labor-intensive, and subjective investigations, leading to inconsistencies in the threat analysis.<n>We propose an automated pipeline that leverages Natural Language Processing (NLP) to minimize human intervention and associated biases.
arXiv Detail & Related papers (2026-01-20T07:31:59Z) - AI Deception: Risks, Dynamics, and Controls [153.71048309527225]
This project provides a comprehensive and up-to-date overview of the AI deception field.<n>We identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception.<n>We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment.
arXiv Detail & Related papers (2025-11-27T16:56:04Z) - Toward Automated Security Risk Detection in Large Software Using Call Graph Analysis [0.30586855806896035]
This paper investigates the automation of software threat modeling through the clustering of call graphs using density-based and community detection algorithms.<n>The proposed method was evaluated through a case study of the Splunk Forwarder Operator (SFO), wherein selected clustering metrics were applied to the software's call graph to assess pertinent code-density security weaknesses.
arXiv Detail & Related papers (2025-10-30T15:43:59Z) - SoK: Advances and Open Problems in Web Tracking [71.54586748169943]
Web tracking is a pervasive and opaque practice that enables personalized advertising, and conversion tracking.<n>Web tracking is undergoing a once-in-a-generation transformation driven by shifts in the advertising industry, the adoption of anti-tracking countermeasures by browsers, and the growing enforcement of emerging privacy regulations.<n>This Systematization of Knowledge (SoK) aims to consolidate and synthesize this wide-ranging research, offering a comprehensive overview of the technical mechanisms, countermeasures, and regulations that shape the modern and rapidly evolving web tracking landscape.
arXiv Detail & Related papers (2025-06-16T23:30:54Z) - Towards Safety and Security Testing of Cyberphysical Power Systems by Shape Validation [42.350737545269105]
complexity of cyberphysical power systems leads to larger attack surfaces to be exploited by malicious actors.<n>We propose to meet those risks with a declarative approach to describe cyber power systems and automatically evaluate security and safety controls.
arXiv Detail & Related papers (2025-06-14T12:07:44Z) - Expert-in-the-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection [38.083049237330826]
This study explores the use of Large Language Models (LLMs) in software vulnerability assessment by simulating the identification of Python code with known Common Weaknessions (CWEs)<n>Our results indicate that while zero-shot prompting performs poorly, few-shot prompting significantly enhances classification performance.<n> challenges such as model reliability, interpretability, and adversarial robustness remain critical areas for future research.
arXiv Detail & Related papers (2025-06-11T18:43:51Z) - Lazarus Group Targets Crypto-Wallets and Financial Data while employing new Tradecrafts [0.0]
This report presents a comprehensive analysis of a malicious software sample, detailing its architecture, behavioral characteristics, and underlying intent.<n>The malware core functionalities, including persistence mechanisms, command-and-control communication, and data exfiltration routines, are identified.<n>This malware analysis report not only reconstructs past adversary actions but also establishes a robust foundation for anticipating and mitigating future attacks.
arXiv Detail & Related papers (2025-05-27T20:13:29Z) - Cyber Defense Reinvented: Large Language Models as Threat Intelligence Copilots [36.809323735351825]
CYLENS is a cyber threat intelligence copilot powered by large language models (LLMs)<n>CYLENS is designed to assist security professionals throughout the entire threat management lifecycle.<n>It supports threat attribution, contextualization, detection, correlation, prioritization, and remediation.
arXiv Detail & Related papers (2025-02-28T07:16:09Z) - Beyond the Surface: An NLP-based Methodology to Automatically Estimate CVE Relevance for CAPEC Attack Patterns [42.63501759921809]
We propose a methodology leveraging Natural Language Processing (NLP) to associate Common Vulnerabilities and Exposure (CAPEC) vulnerabilities with Common Attack Patternion and Classification (CAPEC) attack patterns.<n> Experimental evaluations demonstrate superior performance compared to state-of-the-art models.
arXiv Detail & Related papers (2025-01-13T08:39:52Z) - Bringing Order Amidst Chaos: On the Role of Artificial Intelligence in Secure Software Engineering [0.0]
The ever-evolving technological landscape offers both opportunities and threats, creating a dynamic space where chaos and order compete.<n>Secure software engineering (SSE) must continuously address vulnerabilities that endanger software systems.<n>This thesis seeks to bring order to the chaos in SSE by addressing domain-specific differences that impact AI accuracy.
arXiv Detail & Related papers (2025-01-09T11:38:58Z) - SoK: A Defense-Oriented Evaluation of Software Supply Chain Security [3.165193382160046]
We argue that the next stage of software supply chain security research and development will benefit greatly from a defense-oriented approach.
This paper introduces the AStRA model, a framework for representing fundamental software supply chain elements and their causal relationships.
arXiv Detail & Related papers (2024-05-23T18:53:48Z) - Profile of Vulnerability Remediations in Dependencies Using Graph
Analysis [40.35284812745255]
This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) model.
We analyze control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities.
Results demonstrate the effectiveness of the enhanced GAT model in offering nuanced insights into the relational dynamics of code vulnerabilities.
arXiv Detail & Related papers (2024-03-08T02:01:47Z) - DevPhish: Exploring Social Engineering in Software Supply Chain Attacks on Developers [0.3754193239793766]
adversaries utilize Social Engineering (SocE) techniques specifically aimed at software developers.
This paper aims to comprehensively explore the existing and emerging SocE tactics employed by adversaries to trick Software Engineers (SWEs) into delivering malicious software.
arXiv Detail & Related papers (2024-02-28T15:24:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.