Software Supply Chain Attacks, a Threat to Global Cybersecurity: SolarWinds’ Case Study
Bibliographic record
Abstract
Exploitation of a vulnerability that compromised the source code of the Solar Winds’ Orion system, a software that is used widely by different government and industry actors in the world for the administration and monitoring of networks; brought to the fore a type of stealth attack that has been gaining momentum: supply chain attacks. The main problem in the violation of the software supply chain is that, from 85% to 97% of the code currently used in the software development industry comes from the reuse of open source code frameworks, repositories of third-party software and APIs, creating potential vulnerabilities in the development cycle of a software product. This research analyzes the SolarWinds case study from an exploratory review of academic literature, government information, but also from the articles and reports that are published by different cybersecurity consulting firms and software providers. Then, a set of good practices is proposed such as: Zero trust, Multi-Factor authentication mechanisms (MFA), strategies such as SBOM and the recommendations of the CISA guide to defend against this type of attack. Finally, the research discusses about how to improve response times and prevention against this type of attacks, also future research related to the subject is suggested, such as the application of Machine Learning and Blockchain technologies. Additionally for risk reduction, in addition to the management and articulation of IT teams that participate in all the actors that are part of the software life cycle under a DevSecOps approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".