SoK: A Defense-Oriented Evaluation of Software Supply Chain Security

• URL: http://arxiv.org/abs/2405.14993v2
• Date: Wed, 29 May 2024 14:26:13 GMT
• Title: SoK: A Defense-Oriented Evaluation of Software Supply Chain Security
• Authors: Eman Abu Ishgair, Marcela S. Melara, Santiago Torres-Arias,
• Abstract summary: 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.
• Score: 3.165193382160046
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The software supply chain comprises a highly complex set of operations, processes, tools, institutions and human factors involved in creating a piece of software. A number of high-profile attacks that exploit a weakness in this complex ecosystem have spurred research in identifying classes of supply chain attacks. Yet, practitioners often lack the necessary information to understand their security posture and implement suitable defenses against these attacks. We argue that the next stage of software supply chain security research and development will benefit greatly from a defense-oriented approach that focuses on holistic bottom-up solutions. To this end, this paper introduces the AStRA model, a framework for representing fundamental software supply chain elements and their causal relationships. Using this model, we identify software supply chain security objectives that are needed to mitigate common attacks and systematize knowledge on recent and well-established security techniques for their ability to meet these objectives. We validate our model against prior attacks and taxonomies. Finally, we identify emergent research gaps and propose opportunities to develop novel software development tools and systems that are secure-by-design.

Related papers

• SoK: Analysis of Software Supply Chain Security by Establishing Secure Design Properties [6.1570934202202725]
  This paper systematizes knowledge about secure software supply chain patterns. It identifies four stages of a software supply chain attack and proposes three security properties crucial for a secured supply chain.
  arXiv  Detail & Related papers  (2024-06-14T15:16:09Z)
• 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)
• Assessing the Threat Level of Software Supply Chains with the Log Model [4.1920378271058425]
  The use of free and open source software (FOSS) components in all software systems is estimated to be above 90%. This work presents a novel approach of assessing threat levels in FOSS supply chains with the log model.
  arXiv  Detail & Related papers  (2023-11-20T12:44:37Z)
• Software Repositories and Machine Learning Research in Cyber Security [0.0]
  The integration of robust cyber security defenses has become essential across all phases of software development. Attempts have been made to leverage topic modeling and machine learning for the detection of these early-stage vulnerabilities in the software requirements process.
  arXiv  Detail & Related papers  (2023-11-01T17:46:07Z)
• Software supply chain: review of attacks, risk assessment strategies and security controls [0.13812010983144798]
  The software product is a source of cyber-attacks that target organizations by using their software supply chain as a distribution vector. We analyze the most common software supply chain attacks by providing the latest trend of analyzed attacks. This study introduces unique security controls to mitigate analyzed cyber-attacks and risks by linking them with real-life security incidence and attacks.
  arXiv  Detail & Related papers  (2023-05-23T15:25:39Z)
• CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models [58.27254444280376]
  Large language models (LLMs) for automatic code generation have achieved breakthroughs in several programming tasks. Training data for these models is usually collected from the Internet (e.g., from open-source repositories) and is likely to contain faults and security vulnerabilities. This unsanitized training data can cause the language models to learn these vulnerabilities and propagate them during the code generation procedure.
  arXiv  Detail & Related papers  (2023-02-08T11:54:07Z)
• 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)
• Towards Automated Classification of Attackers' TTPs by combining NLP with ML Techniques [77.34726150561087]
  We evaluate and compare different Natural Language Processing (NLP) and machine learning techniques used for security information extraction in research. Based on our investigations we propose a data processing pipeline that automatically classifies unstructured text according to attackers' tactics and techniques.
  arXiv  Detail & Related papers  (2022-07-18T09:59:21Z)
• Will bots take over the supply chain? Revisiting Agent-based supply chain automation [71.77396882936951]
  Agent-based supply chains have been proposed since early 2000; industrial uptake has been lagging. We find that agent-based technology has matured, and other supporting technologies that are penetrating supply chains are filling in gaps. For example, the ubiquity of IoT technology helps agents "sense" the state of affairs in a supply chain and opens up new possibilities for automation.
  arXiv  Detail & Related papers  (2021-09-03T18:44:26Z)
• Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses [150.64470864162556]
  This work systematically categorizes and discusses a wide range of dataset vulnerabilities and exploits. In addition to describing various poisoning and backdoor threat models and the relationships among them, we develop their unified taxonomy.
  arXiv  Detail & Related papers  (2020-12-18T22:38:47Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)

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.