Backdoor Attacks on Deep Learning Models in Mobile Apps
Attack on AI1 Research / proof of conceptSupply chainAdversarial evasion
Researchers discovered that numerous real-world mobile applications were vulnerable to backdoor attacks through neural payload injection. The empirical study covered critical applications such as face authentication and financial services.
Details
- type
- Attack on AIAn AI model, agent or AI-powered product was the thing exploited, abused or leaked.
- categories
- Supply chainprimaryMalicious or backdoored models, poisoned packages, unsafe deserialization and other compromised AI dependencies.
- Adversarial evasionInputs crafted to make a model misclassify or miss detections (perturbations, evasion of AI-based defences).
- Supply chainprimary
- owasp llm top 10
- mitre atlas
- Both rows are derived from the type and categories (OWASP 2025 edition, ATLAS release 2026.09), not classified per record. How
- severity
- 1 · Research / proof of conceptDemonstrated by researchers; no known real-world exploitation.
- targets
- Enterprise deployment
- products
- Mobile deep learning apps
- incident date
- 18 Jan 2021
- first reported
- 3 Feb 2022
- origin
- MITRE ATLASDerived from MITRE ATLAS (Apache 2.0, © MITRE).
- classifier confidence
- 0.95 · published at or above 0.80; see the methodology
- tags
- backdoor · mobile-apps · neural-payload-injection
- record
- created 2026-10-18 06:55Z · updated 2026-10-18 06:55Z · json
Sources · 2
- 2022-02-03Backdoor Attack on Deep Learning Models in Mobile AppsMITRE ATLAS · atlas.mitre.org/studies/AML.CS0013
- 2022-02-03DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload InjectionarXiv · arxiv.org/abs/2101.06896
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