Publications

Commodity operating system kernels remain monolithic for practical and historical reasons. All kernel code shares a single address space, executes with elevated processor privileges, and has largely unhindered access to all data, including data irrelevant to the completion of a specific task. Applying the principle of least privilege, which limits available resources only to those needed to perform a particular task, to compartmentalize the kernel would realize major security gains, similar to microkernels yet without the major redesign effort. Here, we introduce a compartmentalization design, called a Hardware-Assisted Kernel Compartmentalization (HAKC), that approximates least privilege separation, while minimizing both developer effort and performance overhead. HAKC divides code and data into separate partitions, and specifies an access policy for each partition. Data is owned by a single partition, and a partition’s access-control policy is enforced at runtime, preventing unauthorized data access. When a partition needs to transfer control flow to outside itself, data ownership is transferred to the target, and transferred back upon return. The HAKC design allows for isolating code and data from the rest of the kernel, without utilizing any additional Trusted Computing Base while compartmentalized code is executing. Instead, HAKC relies on hardware for enforcement. Loadable kernel modules (LKMs), which dynamically load kernel code and data providing specialized functionality, are the single largest part of the Linux source base. Unfortunately, their collective size and complexity makes LKMs the cause of the majority of CVEs issued for the Linux kernel. The combination of a large attack surface in kernel modules, and the monolithic design of the Linux kernel, make LKMs ideal candidates for compartmentalization. To demonstrate the effectiveness of our approach, we implement HAKC in Linux v5.10 using extensions to the Arm v8.5-A ISA, and compartmentalize the ipv6.ko LKM, which consists of over 55k LOC. The average overhead measured in Apachebench tests was just 1.6%–24%. Additionally, we compartmentalize the nf_tables.ko packet filtering LKM, and measure the combined impact of using both LKMs. We find a reasonable linear growth in overhead when both compartmentalized LKMs are used. Finally, we measure no significant difference in performance when using the compartmentalized ipv6.ko LKM over the unmodified LKM during real-world web browsing experiments on the Alexa Top 50 websites.

While the number of IoT devices grows at an exhilarating pace their security remains stagnant. Imposing secure coding standards across all vendors is infeasible. Testing individual devices allows an analyst to evaluate their security post deployment. Any discovered vulnerabilities can then be disclosed to the vendors in order to assist them in securing their products. The search for vulnerabilities should ideally be automated for efficiency and furthermore be device-independent for scalability. We present FirmFuzz, an automated device-independent emulation and dynamic analysis framework for Linux-based firmware images. It employs a greybox-based generational fuzzing approach coupled with static analysis and system introspection to provide targeted and deterministic bug discovery within a firmware image. We evaluate FirmFuzz by emulating and dynamically analyzing 32 images (from 27 unique devices) with a network accessible from the host performing the emulation. During testing, FirmFuzz discovered seven previously undisclosed vulnerabilities across six different devices: two IP cameras and four routers. So far, 4 CVE's have been assigned.