NDSS 2025 – A Comprehensive Memory Safety Analysis Of Bootloaders

Why It Matters

Bootloaders sit at the foundation of the secure‑boot chain; flaws can undermine system integrity and enable malicious code execution before the operating system loads. The findings highlight urgent need for hardened bootloader code and proactive testing in the hardware‑software ecosystem.

Key Takeaways

• 39 bootloader bugs found, 38 previously unknown.
• 14 new vulnerabilities discovered in GRUB.
• Some flaws enable secure‑boot bypass.
• Peripheral inputs identified as primary attack vector.
• New fuzzing framework enables large‑scale bootloader testing.

Pulse Analysis

Bootloaders act as the bridge between firmware and the operating system, orchestrating early system initialization and enforcing Secure Boot policies. Because they run with high privileges before any OS defenses are active, any memory‑safety lapse can grant attackers unfettered control. As modern devices demand richer bootloader functionality—supporting network boot, encrypted partitions, and custom user interfaces—the code base expands, inadvertently widening the attack surface and exposing classic C‑level bugs such as buffer overflows and use‑after‑free errors.

The NDSS paper’s methodology combined a comprehensive review of historic bootloader CVEs with a custom fuzzing harness that injects malformed inputs from typical peripherals like storage media and network adapters. This approach uncovered 39 distinct vulnerabilities across nine bootloaders, with 38 being novel discoveries. GRUB, the de‑facto standard for Linux systems, accounted for 14 of these bugs, some of which could subvert Secure Boot by manipulating signature verification routines. The research underscores that malicious peripheral data remains the most effective trigger, emphasizing the need for stricter input validation at the firmware level.

For the industry, the study serves as a wake‑up call to integrate memory‑safety testing into the bootloader development lifecycle. The newly released fuzzing framework offers a scalable solution for vendors to detect latent defects before deployment, potentially reducing the volume of future CVEs. As hardware manufacturers and OS vendors prioritize a zero‑trust boot process, adopting automated analysis tools and embracing safer programming languages could become standard practice, reinforcing the integrity of the entire computing stack.