Using Generative AI to Simulate Fault Injection Attacks

Fault injection attacks are a potent class of hardware-based exploits that introduce intentional errors—such as voltage glitches, laser pulses, clock manipulations, or electromagnetic interference—to alter a system’s behavior. These attacks can bypass authentication, leak cryptographic keys, or compromise firmware integrity.

Traditionally, simulating or emulating fault injection scenarios has required expensive lab equipment, physical access, and deep hardware expertise. But with the rise of Generative AI, especially Large Language Models (LLMs) and multi-modal AI systems, researchers and engineers can now simulate, analyze, and model fault injection attacks virtually, accelerating vulnerability discovery and secure design testing.

Using Generative AI to Simulate Fault Injection Attacks

🧠 What Are Fault Injection Attacks?

Fault injection manipulates the physical environment to cause a system to:

• Skip instructions (e.g., if (auth) becomes a no-op)

• Flip bits in memory or registers

• Corrupt crypto computations

• Trigger undefined states or resets

These faults are often injected using:

• Power glitches (voltage droops)

• Clock frequency tampering

• Laser or EM pulses

• Row hammer-like memory abuse

Use cases include breaking bootloaders, bypassing secure boot, and extracting secrets from secure elements.

🤖 Why Use Generative AI for Simulation?

Generative AI models (especially LLMs and multimodal transformers) offer unique advantages in fault injection research:

• Model behavioral consequences of injected faults

• Generate synthetic attack traces

• Simulate altered instruction flows

• Predict vulnerable execution paths

• Explain attack impact in plain language

These capabilities reduce reliance on hardware labs and accelerate iterative testing of embedded systems.

🛠️ How Generative AI Simulates Fault Injection

1. Instruction-Level Fault Simulation

LLMs trained on assembly and firmware code can:

• Accept a code snippet and a fault model (e.g., “skip instruction at clock cycle 312”)

• Generate a new sequence simulating the fault

• Explain the behavioral change

Prompt example:

“Simulate a voltage glitch during RSA decryption that causes one modular multiplication to be skipped. What would be the resulting vulnerability?”

GPT-4 might respond:

“Skipping a multiplication in CRT-RSA could leak information about the private key due to faulty output detectable via Lenstra's attack.”

2. Synthetic Trace Generation

Using LLMs and time-series generation models, one can simulate:

• Power traces showing fault injection effects

• EM emission deltas

• Timing anomalies from glitch-induced instruction skips

These synthetic datasets can then train machine learning-based SCA (side-channel analysis) detectors.

3. Simulation of Fault Models on Firmware

By feeding decompiled functions to LLMs:

• Predict which parts of firmware are vulnerable to fault skipping

• Automatically simulate patched vs. faulted behavior

• Generate proof-of-concept test cases for fuzzers or emulators

🧪 Example: PIN Authentication Bypass

Given:

if (entered_pin == correct_pin) {
  grant_access();
} else {
  lock_out_user();
}

Prompt GPT-4:

“If a fault causes this if condition to be skipped during execution, what could happen?”

GPT-4:

“The conditional check is skipped. grant_access() may execute regardless of input, resulting in an authentication bypass—common in glitch attacks on smartcards or bootloaders.”

🔐 Use Cases

• Secure boot bypass simulation

• Fault-tolerant crypto validation

• Smartcard attack modeling

• Embedded firmware attack testing

• Security training & education

🔄 Hybrid Workflow (AI + Emulator)

1. Extract firmware or binary code.

2. Use emulators like QEMU or Unicorn Engine to run the code.

3. Inject faults at arbitrary points via GPT-4-guided input mutation or emulation hooks.

4. Observe behavioral shifts.

5. Use GPT-4 to explain or refine the fault model.

⚠️ Challenges and Considerations

• Accuracy: LLM simulations are approximations — physical validation is still required for high-assurance targets.

• Instruction Set Specificity: Custom ISA (Instruction Set Architectures) may need model fine-tuning.

• Security concerns: Simulation tools must be handled ethically — especially when modeling real product vulnerabilities.

🔮 The Future: AI-Augmented Fault Injection Labs

In the near future, we expect:

• AI agents that dynamically explore firmware via fault models

• Simulation sandboxes where AI tests robustness under power, timing, and environmental variations

• Proactive design tools that use AI to inject faults during development to test resilience

✅ Conclusion

Generative AI is transforming how we explore hardware vulnerabilities. By simulating fault injection attacks virtually — with contextual awareness and semantic understanding — LLMs are helping developers and security researchers identify flaws earlier, cheaper, and more efficiently.

While not a replacement for hardware-based testing, AI-powered fault simulation is a powerful complement, enabling proactive security analysis in the software development lifecycle.