Stateful Embedded Fuzzing with Peripheral-Accurate SystemC Virtual Prototypes
- URL: http://arxiv.org/abs/2604.19824v1
- Date: Mon, 20 Apr 2026 12:43:42 GMT
- Title: Stateful Embedded Fuzzing with Peripheral-Accurate SystemC Virtual Prototypes
- Abstract summary: We present a novel framework that integrates AFL++ with a stateful SystemC-TLM virtual prototype to enable realistic fuzzing of embedded software.<n> Fuzzer-generated inputs are injected directly into peripheral models, allowing peripherals to trigger natural side effects such as interrupts and FIFO updates.<n>Results on embedded workloads show that our approach eliminates false positives while maintaining comparable code coverage and execution performance as state-of-the-art tools.
- Score: 0.5359378066251386
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The increasing complexity of embedded software has made comprehensive manual testing impractical, motivating the use of automated techniques such as fuzzing. Coverage-guided fuzzers like AFL++ have shown strong results for conventional software but remain challenging to apply effectively in embedded contexts, where peripheral behaviors play critical roles. Existing approaches either use fast user-mode simulators, sacrificing peripheral realism, or rely on full-system simulators with manual instrumentation, limiting applicability to large-scale software. In this work, we present a novel framework that integrates AFL++ with a stateful SystemC-TLM virtual prototype to enable realistic fuzzing of embedded software. Fuzzer-generated inputs are injected directly into peripheral models, allowing peripherals to trigger natural side effects such as interrupts and FIFO updates. By integrating fuzzing with full-system simulation, our framework advances the effectiveness of pre-silicon testing for embedded systems. Results on embedded workloads show that our approach eliminates false positives while maintaining comparable code coverage and execution performance as state-of-the-art tools.
Related papers
- GASE: Gaussian Splatting-Based Automated System for Reconstructing Embodied-Simulation Environments [63.222920701596564]
Training embodied agents in the real world requires skilled operators and expensive hardware.<n> Simulation environments offer a compelling alternative by enabling large-scale, cost-effective data augmentation.<n>Fastly constructing high-fidelity simulation scenes with a minimal sim-to-real gap has become a critical objective in robot learning.
arXiv Detail & Related papers (2026-06-16T05:00:42Z) - You Don't Need Public Tests to Generate Correct Code [0.9668407688201359]
We show that large language models possess the capability to autonomously construct valid inputs and simulate execution flows for self-correction.<n>We introduce DryRUN, a framework that removes the necessity for ground-truth data by enabling the LLM to iteratively plan, synthesize its own test inputs, and run simulated executions.
arXiv Detail & Related papers (2026-04-23T12:21:03Z) - CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing [51.56484100374058]
CLAIRE is a hybrid end-to-end learning framework that integrates unsupervised deep representation learning with supervised classification for intelligent quality control in smart manufacturing systems.<n>It employs an optimized deep autoencoder to transform raw input into a compact latent space, effectively capturing the intrinsic data structure while suppressing irrelevant or noisy features.<n>The proposed framework highlights the potential of integrating explainable AI with feature-aware regularization for robust fault detection.
arXiv Detail & Related papers (2026-03-06T15:11:58Z) - DiffusionHarmonizer: Bridging Neural Reconstruction and Photorealistic Simulation with Online Diffusion Enhancer [62.18680935878919]
We introduce DiffusionHarmonizer, an online generative enhancement framework that transforms renderings into temporally consistent outputs.<n>At its core is a single-step temporally-conditioned enhancer capable of running in online simulators on a single GPU.
arXiv Detail & Related papers (2026-02-27T15:35:30Z) - Toward Automated Virtual Electronic Control Unit (ECU) Twins for Shift-Left Automotive Software Testing [0.0]
Automotive software increasingly outpaces hardware availability, forcing late integration and expensive hardware-in-the-loop (HiL) bottlenecks.<n>InnoRegioChallenge investigated whether a virtual test and integration environment can reproduce electronic control unit (ECU) behavior early enough to run real software binaries before physical hardware exists.<n>We report a prototype that generates instruction-accurate processor models in SystemC/TLM2.0 using an agentic, feedback-driven workflow.
arXiv Detail & Related papers (2026-02-20T11:03:46Z) - ARTIS: Agentic Risk-Aware Test-Time Scaling via Iterative Simulation [72.78362530982109]
ARTIS, Agentic Risk-Aware Test-Time Scaling via Iterative Simulation, is a framework that decouples exploration from commitment.<n>We show that naive LLM-based simulators struggle to capture rare but high-impact failure modes.<n>We introduce a risk-aware tool simulator that emphasizes fidelity on failure-inducing actions.
arXiv Detail & Related papers (2026-02-02T06:33:22Z) - Full-stack Physics-level model of cascaded entanglement links [1.9014141313309265]
We present a stack of tools for modeling mode by mode a ZALM source under realistic conditions, in isolation, or as a part of a complete network testbed.<n>Our modeling formalism builds upon a hybrid Gaussian and non-Gaussian representation, providing a flexible tradeoff between performance and accuracy.<n>We use this software stack to demonstrate a number of complete networking protocols built upon the ZALM source.
arXiv Detail & Related papers (2025-10-20T18:00:08Z) - Leveraging SystemC-TLM-based Virtual Prototypes for Embedded Software Fuzzing [1.4764499873402919]
SystemC-based virtual prototypes have emerged as widely adopted tools to test software ahead of hardware availability.<n>We present a framework that allows the integration of American-Fuzzy-Lop-based fuzzers and SystemC-based simulators.
arXiv Detail & Related papers (2025-09-01T10:03:11Z) - Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time [57.30651532625017]
We present a novel hybrid method that integrates numerical simulation, neural physics, and generative control.<n>Our system demonstrates robust performance across diverse 2D/3D scenarios, material types, and obstacle interactions.<n>We promise to release both models and data upon acceptance.
arXiv Detail & Related papers (2025-05-25T01:27:18Z) - Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning [50.332027356848094]
AI-based applications are deployed at intelligent controllers to carry out functionalities like scheduling or power control.
The mapping between context and AI model parameters is ideally done in a zero-shot fashion.
This paper introduces a general methodology for the online optimization of AMS mappings.
arXiv Detail & Related papers (2024-06-22T11:17:50Z) - Automating SBOM Generation with Zero-Shot Semantic Similarity [2.169562514302842]
A Software-Bill-of-Materials (SBOM) is a comprehensive inventory detailing a software application's components and dependencies.
We propose an automated method for generating SBOMs to prevent disastrous supply-chain attacks.
Our test results are compelling, demonstrating the model's strong performance in the zero-shot classification task.
arXiv Detail & Related papers (2024-02-03T18:14:13Z) - Controlled time series generation for automotive software-in-the-loop
testing using GANs [0.5352699766206808]
Testing automotive mechatronic systems partly uses the software-in-the-loop approach, where systematically covering inputs of the system-under-test remains a major challenge.
One approach is to craft input sequences which eases control and feedback of the test process but falls short of exposing the system to realistic scenarios.
The other is to replay sequences recorded from field operations which accounts for reality but requires collecting a well-labeled dataset of sufficient capacity for widespread use, which is expensive.
This work applies the well-known unsupervised learning framework of Generative Adrial Networks (GAN) to learn an unlabeled dataset of recorded in-vehicle
arXiv Detail & Related papers (2020-02-16T16:19:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.