DP
D. Peter
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
2 records found
1
Metal-fuzz
A Hardware-in-the-Loop Fuzzing Framework for Hypervisors in Mixed-Criticality Space Applications
Securing modern satellites is increasingly challenging as commercial off-the-shelf components and mixed-criticality workloads make spacecraft software stacks more complex and interconnected. Hypervisors are now used to isolate critical subsystems, yet their privileged position makes them attractive targets and their security has received limited automated scrutiny in space contexts.
This work introduces \textsc{Metal-fuzz}, a coverage-guided, hardware-in-the-loop, bare-metal fuzzing framework that targets paravirtualized hypervisor interfaces under realistic execution conditions. Metal-fuzz bridges common kernel fuzzing backends with embedded targets by forwarding executor operations to a remote system under test, enabling hypercall fuzzing while preserving hardware-specific behavior. We define a threat model centered on guest-to-hypervisor escape and identify key challenges in fuzzing embedded hypervisors, including stateful interfaces, hardware dependence, and coverage collection.
A prototype on a Zynq-7000 (Cortex-A9) platform demonstrates feasibility by fuzzing the XtratuM hypervisor and integrating JTAG-based control and trace-driven coverage. The framework provides a practical foundation for systematic security evaluation of space hypervisors and guides future extensions toward broader device coverage and automated analysis.
...
This work introduces \textsc{Metal-fuzz}, a coverage-guided, hardware-in-the-loop, bare-metal fuzzing framework that targets paravirtualized hypervisor interfaces under realistic execution conditions. Metal-fuzz bridges common kernel fuzzing backends with embedded targets by forwarding executor operations to a remote system under test, enabling hypercall fuzzing while preserving hardware-specific behavior. We define a threat model centered on guest-to-hypervisor escape and identify key challenges in fuzzing embedded hypervisors, including stateful interfaces, hardware dependence, and coverage collection.
A prototype on a Zynq-7000 (Cortex-A9) platform demonstrates feasibility by fuzzing the XtratuM hypervisor and integrating JTAG-based control and trace-driven coverage. The framework provides a practical foundation for systematic security evaluation of space hypervisors and guides future extensions toward broader device coverage and automated analysis.
...
Securing modern satellites is increasingly challenging as commercial off-the-shelf components and mixed-criticality workloads make spacecraft software stacks more complex and interconnected. Hypervisors are now used to isolate critical subsystems, yet their privileged position makes them attractive targets and their security has received limited automated scrutiny in space contexts.
This work introduces \textsc{Metal-fuzz}, a coverage-guided, hardware-in-the-loop, bare-metal fuzzing framework that targets paravirtualized hypervisor interfaces under realistic execution conditions. Metal-fuzz bridges common kernel fuzzing backends with embedded targets by forwarding executor operations to a remote system under test, enabling hypercall fuzzing while preserving hardware-specific behavior. We define a threat model centered on guest-to-hypervisor escape and identify key challenges in fuzzing embedded hypervisors, including stateful interfaces, hardware dependence, and coverage collection.
A prototype on a Zynq-7000 (Cortex-A9) platform demonstrates feasibility by fuzzing the XtratuM hypervisor and integrating JTAG-based control and trace-driven coverage. The framework provides a practical foundation for systematic security evaluation of space hypervisors and guides future extensions toward broader device coverage and automated analysis.
This work introduces \textsc{Metal-fuzz}, a coverage-guided, hardware-in-the-loop, bare-metal fuzzing framework that targets paravirtualized hypervisor interfaces under realistic execution conditions. Metal-fuzz bridges common kernel fuzzing backends with embedded targets by forwarding executor operations to a remote system under test, enabling hypercall fuzzing while preserving hardware-specific behavior. We define a threat model centered on guest-to-hypervisor escape and identify key challenges in fuzzing embedded hypervisors, including stateful interfaces, hardware dependence, and coverage collection.
A prototype on a Zynq-7000 (Cortex-A9) platform demonstrates feasibility by fuzzing the XtratuM hypervisor and integrating JTAG-based control and trace-driven coverage. The framework provides a practical foundation for systematic security evaluation of space hypervisors and guides future extensions toward broader device coverage and automated analysis.
Accelerating hyperbolic t-SNE using the Lorentz Hyperboloid
Exploring a different way to speed up hyperbolic t-SNE
This paper investigates a method for accelerating hyperbolic t-SNE — a popular high-dimensional data visualization technique. In particular, it focuses on building a hyperbolic t-SNE variant that uses a different model of hyperbolic space (called the Lorentz Hyperboloid model) for representing the low-dimensional embeddings. An acceleration algorithm based on a tree data-structure is then used to achieve a better asymptotic runtime complexity compared to the original version. The paper then compares this implementation with other alternatives — including accelerated variants — and shows that it computes embeddings of better quality at a similar rate.
...
This paper investigates a method for accelerating hyperbolic t-SNE — a popular high-dimensional data visualization technique. In particular, it focuses on building a hyperbolic t-SNE variant that uses a different model of hyperbolic space (called the Lorentz Hyperboloid model) for representing the low-dimensional embeddings. An acceleration algorithm based on a tree data-structure is then used to achieve a better asymptotic runtime complexity compared to the original version. The paper then compares this implementation with other alternatives — including accelerated variants — and shows that it computes embeddings of better quality at a similar rate.