What's in a URL? An Analysis of Hardcoded URLs in npm Packages
Bibliographic record
Abstract
npm, a package manager for code written for Node.js, is a core component of the software supply chain, and a critical piece of software infrastructure. By selecting and importing code using npm, software developers can quickly build complex software products that would be extremely expensive to develop from scratch. However, "with great power comes great responsibility'', and the npm ecosystem is a frequent target for attacks, which aim at injecting malicious code in packages. In this paper, we take a look at an understudied internet-based attack surface in npm packages: URLs hardcoded within package code. The presence of such URLs---while often necessary---create risks as package behavior may dependend on data - and even code - retrieved from online endpoints. Unless care is taken to ensure such endpoints remain under control of the package authors, package functionality and security may at later point be compromised. Our analysis of the presence of URLs in the npm ecosystem reveals that problematic URL usage is a present threat, albeit one which is primarily localized within unpopular packages that are infrequently maintained.
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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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".