GHSA-g96x-h6rg-cqq3: ESPectre: Unsafe Deserialization on .npz dataset leads to Code Execution

Published Nov 5, 2026

Updated

ESPectre is a Wi-Fi motion sensing platform for ESP32. Prior to 3.0.0-rc1, Micro-ESPectre analysis tools, including tools/6optimizefilterparams.py, tools/1analyzerawdata.py, and tools/10trainmlmodel.py, load NPZ datasets with NumPy pickle support enabled through micro-espectre/tools/csiutils.py and a direct loader. A malicious dataset contributor or pull-request author can place an object-typed array in a .npz file under micro-espectre/data, causing pickle-controlled code to execute as the maintainer or developer when a documented analysis tool accesses the field. This issue is fixed in version 3.0.0-rc1.

Remediation

Recommended actions to resolve this vulnerability, in priority order.

Upgrade
Upgrade ESPectre to a version that resolves this vulnerability.
Fixed in 3.0.0-rc1

Event History

Nov 5, 2026

CVE Published

via OSV·04:09 PM

Data Sourced

via OSV·04:09 PM

DescriptionSeverityWeakness

Frequently Asked Questions

Who is realistically exposed to this issue?

Maintainers and developers who run the affected Micro-ESPectre analysis or training tools against NPZ datasets are exposed. The risk is especially relevant when datasets can be supplied by contributors or included in pull requests.

What does an attacker need to do to trigger code execution?

An attacker needs to place a crafted NPZ dataset containing an object-typed array under micro-espectre/data. Code executes when a maintainer or developer runs a documented affected tool that accesses the malicious field.

Are users affected by default?

The issue requires a user to process a malicious dataset with an affected analysis tool; the provided information does not indicate that code execution occurs merely from installing or running ESPectre without such a dataset.

What should teams do if they cannot immediately update?

Do not run the identified analysis or training tools on untrusted NPZ datasets, including datasets introduced through pull requests or external contributors. Restrict dataset sources to reviewed, trusted inputs until version 3.0.0-rc1 can be deployed.

How can teams determine whether they may be affected?

Review whether versions prior to 3.0.0-rc1 are in use and whether tools/6_optimize_filter_params.py, tools/1_analyze_raw_data.py, or tools/10_train_ml_model.py have processed contributor-supplied NPZ files. Also inspect micro-espectre/data for NPZ datasets added through untrusted or insufficiently reviewed changes.

GHSA-g96x-h6rg-cqq3: ESPectre: Unsafe Deserialization on .npz dataset leads to Code Execution

Remediation

Event History

Frequently Asked Questions

Who is realistically exposed to this issue?

What does an attacker need to do to trigger code execution?

Are users affected by default?

What should teams do if they cannot immediately update?

How can teams determine whether they may be affected?

Company

Resources

Contact

Frequently Asked Questions

Who is realistically exposed to this issue?

What does an attacker need to do to trigger code execution?

Are users affected by default?

What should teams do if they cannot immediately update?

How can teams determine whether they may be affected?