Model Extraction from Embedded Neural Networks Using Fault Injection and Safe-Error Attacks

Conceitos essenciais

Safe-Error Attacks using fault injection can effectively extract embedded neural network models on 32-bit microcontrollers, even with limited training data, by exploiting the relationship between injected faults and prediction variations.

Resumo

• Bibliographic Information: Hector, K., Moëllic, P.A., Dumont, M., & Dutertre, J.M. (2024). Fault Injection and Safe-Error Attack for Extraction of Embedded Neural Network Models (Accepted SECAI/ESORICS 2023 - Best Paper). arXiv:2308.16703v2 [cs.CR].
• Research Objective: This research paper investigates the feasibility of extracting embedded neural network models on 32-bit microcontrollers using fault injection and Safe-Error Attacks (SEA).
• Methodology: The authors propose a three-step methodology: 1) Crafting an attack dataset of inputs that yield uncertain predictions using a black-box genetic algorithm. 2) Performing SEA with bit-set faults on model parameters while comparing faulted and error-free predictions to recover bit values, further enhanced by the Least Significant Bit Leakage (LSBL) principle. 3) Training a substitute model with limited training data (8%) using recovered bits as constraints in a mean clustering training process.
• Key Findings: The study demonstrates that SEA, combined with LSBL, can recover a significant portion of the most significant bits of the victim model's parameters. Specifically, they achieve over 80% recovery for the 6 most significant bits. Using these recovered bits to constrain the training of a substitute model, they achieve accuracy comparable to the victim model, even with limited training data. For instance, with 90% of the most significant bits recovered, the substitute model for CNN achieves 75.27% accuracy compared to the victim model's 79.4%, and the MLP model achieves 92.93% accuracy compared to 94.94% for the victim model.
• Main Conclusions: The research concludes that SEA with fault injection poses a significant threat to the confidentiality of embedded neural network models on 32-bit microcontrollers. The proposed method effectively extracts a substantial portion of the model's parameters, enabling the training of a high-fidelity substitute model.
• Significance: This research highlights the vulnerability of embedded machine learning models to physical attacks, particularly in the context of increasing deployment of these models in resource-constrained devices.
• Limitations and Future Research: The authors acknowledge the need for further investigation into the impact of model architecture on SEA efficiency and exploration of alternative input generation techniques. Future research should also focus on practical implementations of the attack on various 32-bit microcontroller platforms and explore potential countermeasures.

Estatísticas

For CNN, 14.23% of "Certain" inputs did not lead to any bit recovery.
For MLP, "Uncertain" inputs extracted 64 times more bits on average than "Certain" inputs (8438 vs. 131).
The attack recovered 80% and 90% of the MSB of the CNN parameters with only 150 and 1500 crafted inputs, respectively.
The LSBL principle increased the rate of recovered bits from 47.05% to 80.1% for the CNN model with 5000 crafted inputs.
The recovery error for bits estimated by LSBL decreased to under 1% with only 150 and 300 inputs for CNN and MLP, respectively.
Without training, using only recovered bits, the substitute model achieved a low accuracy of 26.02% for CNN and 75.78% for MLP.
With 90% of MSB recovered, the substitute models achieved 75.27% accuracy for CNN and 92.93% for MLP, with fidelity rates of 85.58% and 96.44%, respectively.
The Accuracy Under Attack (AUA) for the victim models, when using adversarial examples crafted on the substitute models, was 1.83% for CNN and 0% for MLP.
In practical experiments on an ARM Cortex-M3 platform, 90% of MSB were recovered using only 15 crafted inputs.

Citações

"Our work is the first to demonstrate that a well-known attack strategy against cryptographic modules is possible and can reach consistent results regarding the state-of-the-art."
"This work aims at demonstrating that this two-step methodology is actually generalizable to another type of platforms, i.e. 32-bit microcontrollers, with a different fault model (bit-set) and exploitation methods (SEA and input crafting)."
"Our results demonstrate a high rate of recovered bits for both models thanks to SEA associated to the LSBL principle. In the best case, we can estimate about 90% of the most significant bits."
"This research highlights the vulnerability of embedded machine learning models to physical attacks, particularly in the context of increasing deployment of these models in resource-constrained devices."

Principais Insights Extraídos De

Fault Injection and Safe-Error Attack for Extraction of Embedded Neural Network Models

by Kevin Hector... às arxiv.org 11-18-2024

https://arxiv.org/pdf/2308.16703.pdf

Perguntas Mais Profundas

How can the proposed model extraction attack be mitigated in a real-world setting with limited resources on embedded devices?

Mitigating the proposed model extraction attack, which leverages Safe-Error Attacks (SEA) and bit-set fault injections on embedded devices, requires a multi-faceted approach that balances security with the inherent resource constraints of these platforms. Here are some potential mitigation strategies:
1. Randomization and Uncertainty Injection:

Output Feature Map Scaling: As explored in the paper, randomly scaling the output feature maps of intermediate layers during inference introduces uncertainty in the prediction scores. This makes it harder for attackers to establish a stable baseline for comparison during SEA, as even slight variations in scaling can significantly alter the output when faults are injected.
Stochastic Quantization: Instead of using a deterministic quantization scheme, incorporating stochastic rounding during the quantization process can introduce noise, making it difficult for attackers to precisely infer the original bit values from the faulted outputs.
2. Enhancing Fault Resistance:

Critical Parameter Protection: Given limited resources, prioritize protecting the most vulnerable parameters, such as those identified by the attackers as being highly sensitive to bit-flips. This could involve using hardware-level redundancy or more robust memory cells for storing these critical values.
Memory Access Monitoring: Implement lightweight runtime monitoring mechanisms to detect anomalous memory access patterns, such as those associated with laser fault injection. This could involve monitoring the frequency and duration of memory accesses to specific regions.
3. Limiting Attack Surface:

Secure Boot and Code Integrity: Ensure the integrity of the firmware and prevent unauthorized modifications that could disable or weaken security mechanisms. Secure boot processes and code signing can help achieve this.
Physical Protection: While challenging, consider physical hardening techniques to deter direct physical access to the device, making it harder for attackers to perform fault injections.
4. Balancing Security and Performance:

Adaptive Defenses: Dynamically adjust the strength of the defense mechanisms based on the perceived threat level. For instance, increase the frequency of randomization or the sensitivity of memory access monitoring when the device is operating in a high-risk environment.
Resource-Aware Design: Carefully evaluate the computational and memory overhead of each defense mechanism and prioritize those that offer the best security-performance trade-off for the specific embedded platform.
5. Continuous Research and Development:

Novel Defense Mechanisms: Foster ongoing research into new and innovative defense strategies specifically tailored for resource-constrained embedded devices. This includes exploring lightweight cryptographic techniques, side-channel resistant implementations, and novel fault detection mechanisms.
It's crucial to acknowledge that no single defense is foolproof. A layered approach combining multiple strategies is essential to effectively mitigate the evolving threat of model extraction attacks.

Could the reliance on "Uncertain" predictions for successful bit recovery be exploited to develop a defense mechanism that makes it harder for attackers to craft effective inputs?

Yes, the attacker's reliance on "Uncertain" predictions for successful bit recovery using SEA presents an intriguing opportunity for developing defense mechanisms. The core idea is to make it harder for attackers to find or craft inputs that consistently produce these uncertain predictions. Here are some potential avenues:
1. Input Filtering and Transformation:

Uncertainty-Aware Input Sanitization: Develop methods to analyze incoming inputs and identify those likely to produce uncertain predictions. This could involve using a secondary, lightweight model trained to predict the uncertainty of the primary model's output. Inputs flagged as potentially problematic could be rejected or pre-processed to reduce their uncertainty.
Adversarial Input Detection: Leverage techniques from adversarial machine learning to detect inputs that exhibit characteristics common to adversarial examples, which often aim to exploit model uncertainties. This could involve using statistical outlier detection or training a separate model to distinguish between benign and adversarial inputs.
2. Model Hardening for Uncertainty Reduction:

Confidence Calibration: Train the model to be well-calibrated, meaning its predicted probabilities accurately reflect its confidence in its predictions. This can reduce the likelihood of the model assigning high probabilities to multiple classes, thus reducing the occurrence of uncertain predictions.
Robust Training Techniques: Employ robust training methods that aim to improve the model's generalization ability and reduce its sensitivity to small perturbations in the input space. This can make it harder for attackers to find inputs that exploit subtle decision boundaries and lead to uncertain predictions.
3. Dynamic Prediction Thresholding:

Adaptive Confidence Thresholds: Instead of using a fixed threshold for classifying predictions as "Certain" or "Uncertain," dynamically adjust the threshold based on the model's confidence in its predictions for a given input. This can make it harder for attackers to consistently produce inputs that fall within the uncertain region.
4. Combining with Other Defenses:

Defense in Depth: Integrate these uncertainty-aware defenses with other mitigation strategies, such as randomization, fault resistance mechanisms, and physical protections, to create a more comprehensive and resilient security posture.
Challenges and Considerations:

Overhead and Complexity: Implementing these defenses may introduce computational and memory overhead, particularly on resource-constrained embedded devices. Careful optimization and trade-off analysis are crucial.
Evolving Attack Strategies: Attackers may adapt their techniques to circumvent these defenses. Continuous research and development are essential to stay ahead of emerging threats.
By understanding and exploiting the attacker's reliance on uncertain predictions, we can develop more targeted and effective defenses against SEA-based model extraction attacks.

What are the broader ethical implications of increasingly powerful model extraction attacks, and how can we ensure responsible development and deployment of AI models in a security-conscious manner?

The increasing sophistication of model extraction attacks raises significant ethical concerns, particularly as AI models become more deeply integrated into critical infrastructure, sensitive decision-making processes, and our daily lives. Here are some key ethical implications and considerations for responsible AI development and deployment:
1. Intellectual Property Theft and Unfair Advantage:

Protecting Innovation: Model extraction can undermine the significant investments made in developing and training AI models, potentially discouraging innovation and competition in the field.
Fair Use and Attribution: Clear guidelines and legal frameworks are needed to define acceptable use of publicly available models and to ensure proper attribution and compensation for intellectual property.
2. Privacy Violations and Data Security:

Indirect Data Leakage: Even if models themselves don't contain sensitive data, extracted models can be used to infer information about the training data, potentially leading to privacy breaches.
Model Inversion Attacks: Attackers could exploit extracted models to reconstruct or generate synthetic data that closely resembles the original training data, further amplifying privacy risks.
3. Bias Amplification and Discrimination:

Perpetuating Existing Biases: Extracted models may inherit and even amplify biases present in the original training data, leading to unfair or discriminatory outcomes when deployed in sensitive domains like loan applications or criminal justice.
Lack of Transparency and Accountability: The opaque nature of some model extraction techniques makes it challenging to audit and address potential biases in the extracted models.
4. Safety and Security Risks:

Malicious Model Manipulation: Attackers could extract a model, subtly manipulate its behavior, and then redistribute it, potentially causing harm or compromising system safety.
Evasion of Security Mechanisms: Extracted models can be used to understand and circumvent security measures, such as those used for spam detection or fraud prevention.
Ensuring Responsible AI Development and Deployment:
1. Prioritizing Security by Design:

Threat Modeling and Mitigation: Integrate security considerations throughout the entire AI lifecycle, from data collection and model training to deployment and monitoring.
Robust Defense Mechanisms: Invest in research and development of robust defenses against model extraction attacks, including those discussed in previous responses.
2. Promoting Transparency and Explainability:

Model Cards and Documentation: Provide clear and comprehensive documentation of AI models, including their intended use, limitations, and potential biases.
Explainable AI (XAI): Develop and utilize XAI techniques to understand and explain model predictions, making it easier to identify and address potential biases or vulnerabilities.
3. Establishing Ethical Guidelines and Regulations:

Responsible AI Principles: Develop and enforce ethical guidelines for AI development and deployment, addressing issues like fairness, transparency, accountability, and privacy.
Data Protection and Privacy Laws: Strengthen and enforce data protection laws to safeguard against unauthorized data access and use, including data used for training AI models.
4. Fostering Collaboration and Education:

Cross-Disciplinary Collaboration: Encourage collaboration between AI researchers, security experts, ethicists, and policymakers to address the complex challenges posed by model extraction attacks.
Public Awareness and Education: Raise awareness among developers, users, and the general public about the potential risks and ethical implications of AI model extraction.
By proactively addressing these ethical concerns and adopting a security-conscious approach to AI development and deployment, we can harness the transformative potential of AI while mitigating the risks posed by increasingly powerful model extraction attacks.