Microsoft’s cybersecurity arm has taken a major step in demonstrating how artificial intelligence can proactively identify software vulnerabilities buried deep in foundational systems. Using its Security Copilot platform, the company discovered flaws in three widely-used open-source bootloaders—GRUB2, U-Boot, and Barebox—that are essential for initiating operating systems across Linux environments, embedded devices, and cloud infrastructure.
Rather than a surface-level feature, this initiative involved Copilot guiding Microsoft’s researchers through complex source code in a targeted, iterative fashion. The effort revealed exploitable vulnerabilities, including an integer overflow in GRUB2 that could potentially bypass UEFI Secure Boot, a key safeguard in system integrity.
Security Copilot Gets Tactical
Security Copilot wasn’t just passively reviewing code—it was actively steering the investigation. Microsoft’s engineers crafted prompts to explore high-risk areas of bootloader code and used Copilot’s responses to refine their queries in real time. The vulnerability in GRUB2 was found during this feedback loop, specifically when the AI flagged an anomaly in how GRUB2 modules handle memory allocation during relocation with large offsets.
As Microsoft explained in its announcement, “Security Copilot helped expedite vulnerability discovery in the bootloaders by refining and iterating prompts that eventually led to the identification of exploitable issues.”
The vulnerabilities discovered in U-Boot and Barebox, while serious, were found to be less immediately exploitable due to requiring physical access, as noted in reporting by BleepingComputer. Still, Microsoft shared all findings with the respective open-source maintainers for remediation and is working closely with them to coordinate patching efforts.
Reinforcing the Software Supply Chain
Bootloaders, while rarely in the spotlight, are pivotal to modern computing. They execute before the operating system, meaning a vulnerability at this level could allow malicious actors to compromise the system before any conventional defenses activate. This is why Secure Boot—a feature of UEFI—is so critical: it ensures only trusted, signed code runs during startup.
Microsoft emphasized that its AI-assisted process surfaced not only exploitable flaws but also subtle issues such as inconsistent bounds checks and logic gaps. Though not every finding was considered high severity, they collectively contribute to attack surface reduction.
And importantly, the AI-driven workflow provided a structured path to review and triage these issues, saving time for open-source maintainers who may not have enterprise-grade resources at their disposal.
The analysis relied on advanced techniques like natural language processing and machine learning models trained on common vulnerability patterns. These tools allowed Copilot to identify risky code structures that traditional manual audits and fuzzing may have missed.
Inside Microsoft’s Expanding AI Defense Strategy
The bootloader analysis was revealed just a week after Microsoft announced a major expansion of Security Copilot through the addition of specialized AI agents. These agents are built to automate tasks such as phishing detection, vulnerability remediation, identity access optimization, and insider risk analysis.
Each agent is integrated into products like Microsoft Defender, Intune, and Entra. For example, the Vulnerability Remediation Agent proactively prioritizes and responds to emerging issues, while the Threat Intelligence Briefing Agent delivers curated analysis to security teams. These AI models are designed to learn from administrator feedback and refine their accuracy.
Security Copilot’s reach doesn’t end with Microsoft’s in-house tools. Five third-party-developed agents—by OneTrust, Aviatrix, BlueVoyant, Tanium, and Fletch—are being integrated into the broader ecosystem to enhance breach response, alert prioritization, and network root-cause analysis.
This shift is part of Microsoft’s broader move toward autonomous, AI-driven cybersecurity. And given that the company now processes over 84 trillion security signals per day and intercepts around 7,000 password attacks per second, it’s easy to see why.
Can AI Keep Pace with Real-World Threats?
As powerful as these tools are, AI-led discovery still faces limitations. False positives remain a concern, and subtle, context-driven bugs might escape detection if the models aren’t tuned precisely. Microsoft has addressed this by building in feedback loops that allow agents to learn from incorrect classifications and refine their future outputs accordingly.
One of the questions raised by this research is whether the same process can be scaled to other domains. Bootloaders are relatively static and have well-defined structures, but higher-level application code often contains more nuanced flaws. Whether Copilot can maintain accuracy in such scenarios remains to be seen.
There’s also the issue of cost. Security Copilot is currently priced at USD 2,920 per month for enterprise users, making it a high-end option for organizations with complex infrastructures. The pricing reflects its ability to ingest telemetry at scale and respond at machine speed, but adoption among smaller firms may be slower.
Bootloaders Today, Everything Tomorrow?
By uncovering vulnerabilities in the foundational layers of computing, Microsoft’s AI has provided a clear case study of what proactive, AI-assisted security can accomplish. These weren’t surface-level bugs—they were lurking in bootloaders that are core to millions of systems.
With Security Copilot evolving from a helper to an autonomous participant in vulnerability research, the role of AI in cyber defense is no longer speculative. It’s becoming operational. As the platform matures, its value will be measured not only by how fast it can detect flaws but by how effectively it helps organizations—both big and small—secure their stacks from the ground up.
