Fuzzing Zephyr Apps - Struggles of Dynamic Analysis on Embedded Applications - Jayashree Srinivasan

Why It Matters

Effective fuzzing of Zephyr firmware uncovers real‑world vulnerabilities that static checks miss, protecting critical embedded systems from exploitation.

Key Takeaways

• Dynamic fuzzing reveals bugs static analysis often misses in embedded firmware
• Input generation must emulate hardware-specific protocols and sensor feedback loops
• Execution environment creation—hardware-in-loop, emulation, or rehosting—is the biggest hurdle
• Coverage‑guided LLVM‑based LIP fuzzer integrates directly into Zephyr builds
• Hybrid approaches combining hardware loops and high‑fidelity emulation improve bug discovery rates

Summary

The video presents Jayashree Srinivasan’s deep dive into fuzzing Zephyr‑based embedded applications, outlining why dynamic analysis is essential for safety‑critical IoT devices.

She explains core concepts of fuzzing—randomized input injection, coverage collection, crash detection—and contrasts it with static analysis, noting lower false‑positive rates and ability to expose runtime faults such as buffer overflows in real‑time loops.

Srinivasan cites concrete bugs discovered in Zephyr drivers (e.g., unchecked packet length leading to underflow) and highlights research tools like the LIP fuzzer integrated into LLVM, as well as rehosting frameworks (QEMU, Simics) and hardware‑in‑loop loops that emulate peripheral registers.

The discussion underscores that building a faithful execution environment is the primary bottleneck; hybrid strategies that blend hardware‑in‑loop testing with high‑fidelity emulation can scale fuzzing across diverse MCU families, accelerating vulnerability discovery for manufacturers and regulators.

Original Description

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Fuzzing Zephyr Apps - Struggles of Dynamic Analysis on Embedded Applications - Jayashree Srinivasan, Analog Devices

Fuzzing, a type of dynamic analysis, is a testing method to find security flaws in software during execution. It involves providing randomized inputs to the application and observing for crashes.

Embedded applications present unique fuzzing challenges. Unlike general-purpose software, they run continuously in real-time without terminating, making it hard to use traditional fuzzing approaches. They receive inputs through specialized peripherals or direct memory/register accesses that require accurate modeling. Fuzzers must generate inputs satisfying highly constrained validation checks while maintaining application state, and crash detection is complicated by the lack of clear program termination.

Existing solutions use hardware, emulation, or rehosted systems with modeled peripherals, employing full source code level, binary-only or API-level fuzzing. Zephyr's current libFuzzer integration targets unit-level API fuzzing but misses system-wide bugs. We aim to integrate AFL++, a popular fuzzing engine, to create a generalized fuzzing strategy across Zephyr's supported platforms. Though still in development, we're exploring the optimal approach to achieve this integration.