Investigating White-Box Attacks for On-Device Models: Reverse Engineering Framework Revealed

Keskeiset käsitteet

The author argues that existing on-device attacking approaches underestimate the harm of attacks due to non-debuggable models, proposing a Reverse Engineering framework to enable white-box attacks effectively.

Tiivistelmä

Numerous mobile apps leverage deep learning capabilities, making on-device models vulnerable to attacks. Existing methods generate black-box attacks, underestimating the threat. The proposed Reverse Engineering framework enables automated transformation of TFLite models for white-box attacks, achieving higher success rates with smaller perturbations.

Key points:

• Mobile devices ideal for deploying DL models.
• On-device models face security threats due to easy extraction.
• Existing methods rely on surrogate models for black-box attacks.
• Proposed REOM framework automates transformation for white-box attacks.
• Results show increased attack success rates with smaller perturbations.

Tilastot

Our approach is effective in achieving automated transformation (92.6%).
REOM enables attackers to achieve higher attack success rates (10.23%→89.03%) with smaller perturbations (1.0→0.01).

Lainaukset

"The proposed REOM can successfully transform over 90% of TFLite models to debuggable models." - Author
"Existing tools cannot achieve the purpose of transforming TFLite models into debuggable ones." - Author

Tärkeimmät oivallukset

Investigating White-Box Attacks for On-Device Models

by Mingyi Zhou,... klo arxiv.org 03-04-2024

https://arxiv.org/pdf/2402.05493.pdf

Syvällisempiä Kysymyksiä

How can developers enhance model deployment strategies considering the vulnerability highlighted by the study?

Developers can enhance model deployment strategies by incorporating white-box evaluation methods into their security protocols. By understanding the vulnerabilities of on-device models and the potential for white-box attacks highlighted in the study, developers can prioritize security measures that focus on evaluating models from a white-box perspective. This includes implementing tools like REOM to automatically transform on-device models into debuggable versions, enabling them to assess vulnerabilities more effectively. Additionally, developers should consider deploying robust encryption techniques and access controls to protect sensitive model information from being extracted or manipulated.

What are the potential implications of underestimating the harm of on-device attacks?

Underestimating the harm of on-device attacks can have severe consequences for both users and organizations utilizing mobile deep learning models. If developers fail to recognize the full extent of vulnerabilities in on-device models and underestimate the effectiveness of white-box attack strategies, they may inadvertently expose sensitive data and compromise user privacy. This could lead to unauthorized access, data breaches, financial losses, reputational damage, and legal repercussions for companies deploying vulnerable models. Furthermore, underestimating these risks may result in inadequate security measures being implemented, leaving systems open to exploitation by malicious actors.

How can reverse engineering frameworks like REOM impact future research and development in mobile DL security?

Reverse engineering frameworks like REOM have significant implications for future research and development in mobile deep learning (DL) security. These frameworks enable researchers and practitioners to analyze complex DL models deployed on mobile devices more comprehensively by transforming non-debuggable on-device models into debuggable versions. This advancement allows for more accurate vulnerability assessments through white-box attack simulations.
In addition:

Researchers can use reverse engineering frameworks like REOM as a foundation for developing new methodologies to evaluate model reliability.
The insights gained from using such tools can inform best practices for securing mobile DL applications against sophisticated cyber threats.
By enhancing our understanding of how attackers exploit vulnerabilities in on-device models through reverse engineering techniques, we can proactively strengthen defenses against emerging threats in mobile DL security.
Overall:
Reverse engineering frameworks like REOM play a crucial role in advancing research efforts aimed at improving cybersecurity measures within mobile DL environments. Their impact extends beyond current studies by providing a framework that empowers researchers with enhanced capabilities to address evolving challenges posed by malicious activities targeting mobile deep learning systems.