Mobile Deep Learning Apps Vulnerable to Neural Payload Injection

# Mobile Deep Learning Apps Vulnerable to Neural Payload Injection

https://gcdradar.vercel.app/incidents/mobile-deep-learning-apps-vulnerable-to-neural-payload-injec · AIR-2023-0012 · closed 2023-04-14

- Lane: Research
- Severity: 1 (Research / proof of concept)
- Fix status: Fix status unknown
- First reported: 2023-03-31
- AI role: Attack on AI
- Products: Mobile Deep Learning Apps

## Summary

Researchers found that many deep learning models in Google Play apps are vulnerable to backdoor attacks via neural payload injection. The empirical study identified multiple critical applications affected by this vulnerability.

## What to do

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

## Latest updates

- 2023-03-31: DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection (https://arxiv.org/abs/2101.06896)
- 2023-03-31: Backdoor Attack on Deep Learning Models in Mobile Apps (https://atlas.mitre.org/studies/AML.CS0013)
- 2023-03-31: AVID-2023-V013: Deep learning models are increasingly used in mobile applications as critical components. (https://github.com/avidml/avid-db/blob/main/vulnerabilities/2023/AVID-2023-V013.json)

## Sources

- 2023-03-31 AVID: AVID-2023-V013: Deep learning models are increasingly used in mobile applications as critical components. https://github.com/avidml/avid-db/blob/main/vulnerabilities/2023/AVID-2023-V013.json
- 2023-03-31 MITRE ATLAS: Backdoor Attack on Deep Learning Models in Mobile Apps https://atlas.mitre.org/studies/AML.CS0013
- 2023-03-31 arXiv: DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection https://arxiv.org/abs/2101.06896

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