Enhancing Software Supply Chain Security Through STRIDE-Based Threat Modelling of CI/CD Pipelines
- URL: http://arxiv.org/abs/2506.06478v1
- Date: Fri, 06 Jun 2025 19:06:59 GMT
- Title: Enhancing Software Supply Chain Security Through STRIDE-Based Threat Modelling of CI/CD Pipelines
- Authors: Sowmiya Dhandapani,
- Abstract summary: This study applies a structured threat modeling approach to identify and mitigate risks throughout the Continuous Integration/ Continuous Deployment lifecycle.<n>Threats are documented and to comprehensive security controls drawn from standards like NIST SP 800-218, Top 10 CI/CD risks, and the SLSA framework.<n>This approach provides a pragmatic roadmap for enhancing CI/CD pipeline security against evolving software supply chain threats.
- Score: 1.3535770763481907
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the increasing adoption of Continuous Integration and Continuous Deployment pipelines, securing software supply chains has become a critical challenge for modern DevOps teams. This study addresses these challenges by applying a structured threat modeling approach to identify and mitigate risks throughout the CI/CD lifecycle. By modeling a representative pipeline architecture incorporating tools such as GitHub, Jenkins, Docker, and Kubernetes and applying the STRIDE framework, we systematically analyze vulnerabilities at each stage, from source code management to deployment. Threats are documented and mapped to comprehensive security controls drawn from standards like NIST SP 800-218, OWASP Top 10 CI/CD risks, and the SLSA framework. Controls are further evaluated against SLSA maturity levels to assess improvements in trust and provenance. To operationalize these findings, the study outlines a practical security toolchain integration strategy grounded in Security as Code and Shift Left-Shield Right principles, enabling automated, enforceable security across the pipeline. This approach provides a pragmatic roadmap for enhancing CI/CD pipeline security against evolving software supply chain threats.
Related papers
- Secure Code Generation via Online Reinforcement Learning with Vulnerability Reward Model [60.60587869092729]
Large language models (LLMs) are increasingly used in software development, yet their tendency to generate insecure code remains a major barrier to real-world deployment.<n>We propose SecCoderX, an online reinforcement learning framework for functionality-preserving secure code generation.
arXiv Detail & Related papers (2026-02-07T07:42:07Z) - 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) - CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents [60.98294016925157]
AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss.<n>We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content.<n>Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks.
arXiv Detail & Related papers (2026-01-14T23:06:35Z) - Agentic AI for Autonomous Defense in Software Supply Chain Security: Beyond Provenance to Vulnerability Mitigation [0.0]
The current paper includes an example of agentic artificial intelligence (AI) based on autonomous software supply chain security.<n>It combines large language model (LLM)-based reasoning, reinforcement learning (RL), and multi-agent coordination.<n>Results show that agentic AI can facilitate the transition to self defending, proactive software supply chains.
arXiv Detail & Related papers (2025-12-29T14:06:09Z) - OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows [77.95511352806261]
Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms.<n>We propose OS-Sentinel, a novel hybrid safety detection framework that combines a Formal Verifier for detecting explicit system-level violations with a Contextual Judge for assessing contextual risks and agent actions.
arXiv Detail & Related papers (2025-10-28T13:22:39Z) - Towards a Blockchain-Based CI/CD Framework to Enhance Security in Cloud Environments [0.1784233255402269]
This research focuses on security issues within Continuous Integration and Deployment (CI/CD) pipelines in a cloud platform as a reaction to recent cyber breaches.<n>This research proposes a blockchain-based solution to enhance CI/CD pipeline security.
arXiv Detail & Related papers (2025-10-17T15:41:40Z) - UpSafe$^\circ$C: Upcycling for Controllable Safety in Large Language Models [67.91151588917396]
Large Language Models (LLMs) have achieved remarkable progress across a wide range of tasks, but remain vulnerable to safety risks such as harmful content generation and jailbreak attacks.<n>We propose UpSafe$circ$C, a unified framework for enhancing LLM safety through safety-aware upcycling.<n>Our results highlight a new direction for LLM safety: moving from static alignment toward dynamic, modular, and inference-aware control.
arXiv Detail & Related papers (2025-10-02T16:43:33Z) - Docker under Siege: Securing Containers in the Modern Era [0.0]
This paper investigates key areas of container security, including runtime protection, network safeguards, configuration best practices, supply chain security, and comprehensive monitoring and logging solutions.<n>We identify common vulnerabilities within these domains and provide actionable recommendations to address and mitigate these risks.
arXiv Detail & Related papers (2025-05-31T13:00:52Z) - Automating Safety Enhancement for LLM-based Agents with Synthetic Risk Scenarios [77.86600052899156]
Large Language Model (LLM)-based agents are increasingly deployed in real-world applications.<n>We propose AutoSafe, the first framework that systematically enhances agent safety through fully automated synthetic data generation.<n>We show that AutoSafe boosts safety scores by 45% on average and achieves a 28.91% improvement on real-world tasks.
arXiv Detail & Related papers (2025-05-23T10:56:06Z) - Shape it Up! Restoring LLM Safety during Finetuning [66.46166656543761]
Finetuning large language models (LLMs) enables user-specific customization but introduces critical safety risks.<n>We propose dynamic safety shaping (DSS), a framework that uses fine-grained safety signals to reinforce learning from safe segments of a response while suppressing unsafe content.<n>We present STAR-DSS, guided by STAR scores, that robustly mitigates finetuning risks and delivers substantial safety improvements across diverse threats, datasets, and model families.
arXiv Detail & Related papers (2025-05-22T18:05:16Z) - Software Security Mapping Framework: Operationalization of Security Requirements [12.04694982718246]
The Software Security Mapping Framework is a structured solution designed to operationalize security requirements across hierarchical levels.<n>The framework systematically maps 131 refined security requirements to over 400 actionable operational steps spanning the software development lifecycle.<n>It is grounded in four core security goals: Secure Software Environment, Secure Software Development, Software Traceability, and Vulnerability Management.
arXiv Detail & Related papers (2025-05-22T06:34:48Z) - ACRIC: Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
Recent security incidents in safety-critical industries exposed how the lack of proper message authentication enables attackers to inject malicious commands or alter system behavior.<n>These shortcomings have prompted new regulations that emphasize the pressing need to strengthen cybersecurity.<n>We introduce ACRIC, a message authentication solution to secure legacy industrial communications.
arXiv Detail & Related papers (2024-11-21T18:26:05Z) - Global Challenge for Safe and Secure LLMs Track 1 [57.08717321907755]
The Global Challenge for Safe and Secure Large Language Models (LLMs) is a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO)
This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks.
arXiv Detail & Related papers (2024-11-21T08:20:31Z) - Advancing Software Security and Reliability in Cloud Platforms through AI-based Anomaly Detection [0.5599792629509228]
This research aims to enhance CI/CD pipeline security by implementing anomaly detection through AI support.
The goal is to identify unusual behaviour or variations from network traffic patterns in pipeline and cloud platforms.
We implemented a combination of Convolution Neural Network(CNN) and Long Short-Term Memory (LSTM) to detect unusual traffic patterns.
arXiv Detail & Related papers (2024-11-14T05:45:55Z) - EARBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [53.717918131568936]
Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction.<n>Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results.<n>However, the deployment of these agents in physical environments presents significant safety challenges.<n>This study introduces EARBench, a novel framework for automated physical risk assessment in EAI scenarios.
arXiv Detail & Related papers (2024-08-08T13:19:37Z) - Enhancing Software Supply Chain Resilience: Strategy For Mitigating Software Supply Chain Security Risks And Ensuring Security Continuity In Development Lifecycle [0.0]
This article delves into the strategic approaches and preventive measures necessary to safeguard the software supply chain against evolving threats.
It aims to foster an understanding of the challenges and vulnerabilities inherent in software supply chain resilience.
The article contributes to the ongoing effort to strengthen the security posture of software supply chains.
arXiv Detail & Related papers (2024-07-08T18:10:47Z) - 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) - Securing the Open RAN Infrastructure: Exploring Vulnerabilities in Kubernetes Deployments [60.51751612363882]
We investigate the security implications of and software-based Open Radio Access Network (RAN) systems.
We highlight the presence of potential vulnerabilities and misconfigurations in the infrastructure supporting the Near Real-Time RAN Controller (RIC) cluster.
arXiv Detail & Related papers (2024-05-03T07:18:45Z) - Evaluating Model-free Reinforcement Learning toward Safety-critical
Tasks [70.76757529955577]
This paper revisits prior work in this scope from the perspective of state-wise safe RL.
We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection.
To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
arXiv Detail & Related papers (2022-12-12T06:30:17Z)
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.