Fuzzing IoT Devices Using Large Language Models (LLMs): A New Paradigm in Security Testing

The explosion of Internet of Things (IoT) devices—ranging from smart thermostats and medical implants to industrial sensors and connected vehicles—has dramatically increased the attack surface for cyber threats. These devices often operate in constrained environments, run proprietary firmware, and lack robust security mechanisms. Traditional vulnerability testing techniques like fuzzing have been essential in identifying flaws. But now, Large Language Models (LLMs) are revolutionizing how fuzzing is performed—introducing automation, context-awareness, and greater precision in uncovering vulnerabilities.

Fuzzing IoT Devices Using Large Language Models (LLMs)

What Is Fuzzing?

Fuzzing is a dynamic testing technique that feeds malformed, unexpected, or random data into a system’s inputs to trigger faults such as crashes, memory leaks, or logic errors. For IoT devices, fuzzing can be applied to:

• Network protocols (e.g., MQTT, CoAP, Zigbee)

• Hardware interfaces (UART, SPI)

• Firmware APIs

• Web management interfaces

The goal is to uncover unknown vulnerabilities that attackers could exploit—especially zero-day bugs.

How LLMs Enhance IoT Fuzzing

Traditional fuzzing relies heavily on random mutations or predefined templates, often requiring deep manual effort to tailor fuzzing payloads for each device or protocol. LLMs like GPT-4 or Claude offer significant improvements by introducing intelligent automation:

1. Protocol-Aware Input Generation

LLMs can understand documentation or examples of IoT protocols and generate realistic and edge-case payloads based on:

• Packet formats

• Field constraints

• Typical command sequences

Instead of blindly mutating bytes, LLMs can craft inputs that intelligently violate protocol rules, making fuzzing more effective.

2. Firmware Interaction Understanding

By analyzing disassembled firmware or decompiled code, LLMs can:

• Identify input handling functions

• Suggest test vectors for specific memory locations

• Reverse-engineer undocumented commands

This reduces reliance on full reverse engineering and speeds up the discovery of vulnerable code paths.

3. Natural Language to Test Cases

Security researchers can describe testing goals in plain English, e.g., “Test buffer overflows in MQTT CONNECT packet”, and the LLM can generate:

• Code for a custom fuzzer

• Specific malformed packet structures

• An execution strategy

This bridges the gap between intention and execution.

Example Use Cases

• Smart Home Hubs: Using LLMs to analyze UPnP protocol usage and generate malformed SOAP requests that trigger buffer overflows or authentication bypasses.

• Industrial IoT (IIoT): Crafting fuzzing inputs for Modbus/TCP or OPC UA commands to disrupt operations in programmable logic controllers (PLCs).

• Medical Devices: Identifying malformed BLE (Bluetooth Low Energy) packets that cause unexpected resets or memory corruption.

Combining LLMs with Fuzzing Frameworks

LLMs can augment established fuzzing tools like:

• Boofuzz or Peach Fuzzer (for protocol fuzzing)

• AFL or LibFuzzer (for binary-level fuzzing)

• Ghidra or Radare2 (for firmware reverse engineering)

By integrating with these frameworks, LLMs can:

• Generate smart input corpora

• Guide mutation strategies

• Interpret crash logs and suggest root causes

Challenges and Limitations

Despite their potential, LLM-driven fuzzing comes with caveats:

• Accuracy: LLM-generated payloads may not always adhere to precise byte-level formatting unless constrained carefully.

• Context Sensitivity: Without a full hardware simulation, LLMs might misinterpret the execution context.

• Security Risks: Malicious use of LLMs for automated vulnerability discovery could lower the barrier for attackers.

Mitigating these challenges involves combining LLMs with symbolic execution, emulators, or hardware-in-the-loop (HIL) setups.

Future Directions

• LLM Fine-tuning: Training models specifically on firmware, exploit patterns, and protocol specs to increase precision.

• Conversational Fuzzing Assistants: Interactive agents that help researchers define and refine fuzzing campaigns.

• Automated Patch Recommendation: LLMs not only find bugs but suggest code-level mitigations.

Conclusion

Fuzzing IoT devices with the help of LLMs represents a transformative step in proactive cybersecurity. By blending natural language understanding with binary and protocol intelligence, LLMs allow for smarter, faster, and more targeted fuzzing. As IoT ecosystems grow more complex and interconnected, leveraging LLMs can become a critical advantage in securing the future of embedded computing.