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Artifact Evaluation Package

This repository contains the full artifact package for the paper:

“On the (In)Security of Loading Machine Learning Models” (former title: “When Secure Isn’t: Assessing the Security of Machine Learning Model Sharing”) (IEEE S&P 2026).

The package is organized to support artifact evaluation along three dimensions:

  • Availability: raw data, scripts, notebooks, PoCs, and model artifacts are included.
  • Reproducibility: results can be recomputed from the provided artifacts (survey statistics/tests, plots, and PoC executions).
  • Functionality: scripts and PoCs run as intended and produce the expected outputs.

Mapping between folders and paper sections/results

1) Vulnerability PoCs

  • Folder: vulnerabilities/
  • Paper mapping:
    • KV1, KV2, KV3Section 4.1
    • SV1, SV2, SV3Section 4.2
  • Goal: PoCs achieve arbitrary code execution at model load time, despite framework-level security measures (e.g., Keras safe_mode), with success indicated by spawning /bin/sh during loading.

Each vulnerability subfolder includes:

  • a README.md with instructions,
  • a report.md snapshot,
  • a docker/ environment,
  • and the PoC artifacts/scripts.

2) Hugging Face scanning experiments

  • Folder: HF_experiments/
  • Paper mapping:
    • Section 4.3, Table 3
  • Goal: availability of all PoC artifacts used for the Hugging Face tests.

3) Survey analysis

  • Folder: survey/
  • Paper mapping:
    • Section 5 (UP2)
  • Goal: the provided raw responses and analysis artifacts reproduce the reported survey statistics, plots, and Wilcoxon perception-shift results.
  • Contains:
    • raw survey CSV,
    • survey form copy,
    • analysis/plot scripts,
    • notebook versions,
    • Wilcoxon perception-shift test notebook.

4) Keras version adoption study

  • Folder: version_adoption_keras/
  • Paper mapping:
    • Appendix B, Figure 2
  • Goal: the provided query output and plotting artifacts regenerate the same Keras version-adoption trend shown in Figure 2.
  • Contains:
    • BigQuery SQL query,
    • raw CSV export,
    • script and notebook to regenerate the plot.

Reports and updates

Contacts

For questions, clarifications, or collaboration inquiries:

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Artifacts supporting the paper "On the (In)Security of Loading Machine Learning Models" (IEEE S&P 2026). Includes proof-of-concept exploits, model artifacts, survey data, and analysis scripts.

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