Black-Box Adversarial Example Attacks on ML Election Systems

# Black-Box Adversarial Example Attacks on ML Election Systems

https://gcdradar.vercel.app/incidents/black-box-adversarial-example-attacks-on-ml-election-systems · AIR-2026-1729 · live

- Lane: Research
- Severity: 1 (Research / proof of concept): Research paper demonstrating black-box adversarial evasion attacks against election ML classifiers.
- Fix status: Fix status unknown
- First reported: 2026-11-03
- AI role: Attack on AI

## Summary

Researchers demonstrate black-box adversarial example attacks against machine learning-based ballot classification models used in election systems. The work proves that classifiers can be manipulated to alter election outcomes without requiring white-box access to model parameters.

## What to do

No action recorded; read the sources below and the vendor's advisory.

## Latest updates

- 2026-11-03: Black-Box Adversarial Example Attacks on Machine Learning based Election Systems (https://arxiv.org/abs/2611.02166)

## Sources

- 2026-11-03 arXiv: Black-Box Adversarial Example Attacks on Machine Learning based Election Systems https://arxiv.org/abs/2611.02166

Verify against the vendor's advisory before changing production. Data CC BY-SA 4.0, https://gcdradar.vercel.app.