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Patch-to-PoC: A Systematic Study of Agentic LLM Systems for Linux Kernel N-Day Reproduction
Authors:
Juefei Pu,
Xingyu Li,
Zhengchuan Liang,
Jonathan Cox,
Yifan Wu,
Kareem Shehada,
Arrdya Srivastav,
Zhiyun Qian
Abstract:
Autonomous large language model (LLM) based systems have recently shown promising results across a range of cybersecurity tasks. However, there is no systematic study on their effectiveness in autonomously reproducing Linux kernel vulnerabilities with concrete proofs-of-concept (PoCs). Owing to the size, complexity, and low-level nature of the Linux kernel, such tasks are widely regarded as partic…
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Autonomous large language model (LLM) based systems have recently shown promising results across a range of cybersecurity tasks. However, there is no systematic study on their effectiveness in autonomously reproducing Linux kernel vulnerabilities with concrete proofs-of-concept (PoCs). Owing to the size, complexity, and low-level nature of the Linux kernel, such tasks are widely regarded as particularly challenging for current LLM-based approaches.
In this paper, we present the first large-scale study of LLM-based Linux kernel vulnerability reproduction. For this purpose, we develop K-Repro, an LLM-based agentic system equipped with controlled code-browsing, virtual machine management, interaction, and debugging capabilities. Using kernel security patches as input, K-Repro automates end-to-end bug reproduction of N-day vulnerabilities in the Linux kernel. On a dataset of 100 real-world exploitable Linux kernel vulnerabilities collected from KernelCTF, our results show that K-Repro can generate PoCs that reproduce over 50\% of the cases with practical time and monetary cost.
Beyond aggregate success rates, we perform an extensive study of effectiveness, efficiency, stability, and impact factors to explain when agentic reproduction succeeds, where it fails, and which components drive performance. These findings provide actionable guidance for building more reliable autonomous security agents and for assessing real-world N-day risk from both offensive and defensive perspectives.
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Submitted 18 February, 2026; v1 submitted 6 February, 2026;
originally announced February 2026.
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Rethinking Kernel Program Repair: Benchmarking and Enhancing LLMs with RGym
Authors:
Kareem Shehada,
Yifan Wu,
Wyatt D. Feng,
Adithya Iyer,
Gryphon Kumfert,
Yangruibo Ding,
Zhiyun Qian
Abstract:
Large Language Models (LLMs) have revolutionized automated program repair (APR) but current benchmarks like SWE-Bench predominantly focus on userspace applications and overlook the complexities of kernel-space debugging and repair. The Linux kernel poses unique challenges due to its monolithic structure, concurrency, and low-level hardware interactions. Prior efforts such as KGym and CrashFixer ha…
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Large Language Models (LLMs) have revolutionized automated program repair (APR) but current benchmarks like SWE-Bench predominantly focus on userspace applications and overlook the complexities of kernel-space debugging and repair. The Linux kernel poses unique challenges due to its monolithic structure, concurrency, and low-level hardware interactions. Prior efforts such as KGym and CrashFixer have highlighted the difficulty of APR in this domain, reporting low success rates or relying on costly and complex pipelines and pricey cloud infrastructure. In this work, we introduce RGym, a lightweight, platform-agnostic APR evaluation framework for the Linux kernel designed to operate on local commodity hardware. Built on RGym, we propose a simple yet effective APR pipeline leveraging specialized localization techniques (e.g., call stacks and blamed commits) to overcome the unrealistic usage of oracles in KGym. We test on a filtered and verified dataset of 143 bugs. Our method achieves up to a 43.36% pass rate with GPT-5 Thinking while maintaining a cost of under $0.20 per bug. We further conduct an ablation study to analyze contributions from our proposed localization strategy, prompt structure, and model choice, and demonstrate that feedback-based retries can significantly enhance success rates.
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Submitted 19 November, 2025;
originally announced November 2025.
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Going Beyond Nouns With Vision & Language Models Using Synthetic Data
Authors:
Paola Cascante-Bonilla,
Khaled Shehada,
James Seale Smith,
Sivan Doveh,
Donghyun Kim,
Rameswar Panda,
Gül Varol,
Aude Oliva,
Vicente Ordonez,
Rogerio Feris,
Leonid Karlinsky
Abstract:
Large-scale pre-trained Vision & Language (VL) models have shown remarkable performance in many applications, enabling replacing a fixed set of supported classes with zero-shot open vocabulary reasoning over (almost arbitrary) natural language prompts. However, recent works have uncovered a fundamental weakness of these models. For example, their difficulty to understand Visual Language Concepts (…
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Large-scale pre-trained Vision & Language (VL) models have shown remarkable performance in many applications, enabling replacing a fixed set of supported classes with zero-shot open vocabulary reasoning over (almost arbitrary) natural language prompts. However, recent works have uncovered a fundamental weakness of these models. For example, their difficulty to understand Visual Language Concepts (VLC) that go 'beyond nouns' such as the meaning of non-object words (e.g., attributes, actions, relations, states, etc.), or difficulty in performing compositional reasoning such as understanding the significance of the order of the words in a sentence. In this work, we investigate to which extent purely synthetic data could be leveraged to teach these models to overcome such shortcomings without compromising their zero-shot capabilities. We contribute Synthetic Visual Concepts (SyViC) - a million-scale synthetic dataset and data generation codebase allowing to generate additional suitable data to improve VLC understanding and compositional reasoning of VL models. Additionally, we propose a general VL finetuning strategy for effectively leveraging SyViC towards achieving these improvements. Our extensive experiments and ablations on VL-Checklist, Winoground, and ARO benchmarks demonstrate that it is possible to adapt strong pre-trained VL models with synthetic data significantly enhancing their VLC understanding (e.g. by 9.9% on ARO and 4.3% on VL-Checklist) with under 1% drop in their zero-shot accuracy.
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Submitted 30 August, 2023; v1 submitted 30 March, 2023;
originally announced March 2023.