NICE: A Framework for Declarative and Machine-Checkable Vulnerability Reproduction
- URL: http://arxiv.org/abs/2606.00625v1
- Date: Sat, 30 May 2026 08:59:40 GMT
- Title: NICE: A Framework for Declarative and Machine-Checkable Vulnerability Reproduction
- Abstract summary: NICE, the NIx CvE reproduction framework, uses declarative recipes to build and automatically validate vulnerable environments.<n>We evaluate NICE on 19 diverse real-world CVEs spanning multiple CWE categories, attack vectors, and target types.<n>NICE is applicable to security education and training (e.g., creating cyber ranges), but also to vulnerability reporting.
- Score: 7.136050731286663
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Reproducing software vulnerabilities is fundamental to security researchers, open-source maintainers, and educators. Yet, vulnerabilities remain hard to reproduce today, and even when they can be reproduced, recreating a software environment where the vulnerability can be exploited becomes harder and harder over time. We present NICE, the NIx CvE reproduction framework, which uses declarative recipes to build and automatically validate vulnerable environments. In NICE, a reproduced CVE comprises one or more NixOS virtual machine configurations, a scripted exploitation scenario, and machine-checkable assertions that provide factual evidence of exploitation. This design facilitates sharing, validation, review, and long-term reproducibility. We evaluate NICE on 19 diverse real-world CVEs spanning multiple CWE categories, attack vectors, and target types (user-space, system software, kernel, and graphical applications). We show that NICE allows to produce concise recipes and integration tests that reproduce vulnerable environments and provide proofs of exploitation. NICE is applicable to security education and training (e.g., creating cyber ranges), but also to vulnerability reporting, where its reproducibility and reviewability properties can make reports easier to audit and verify.
Related papers
- Vulnerability Localization Benchmark: Measuring Agentic Security Analysis at Repository Scale [54.83971397981572]
Language-model agents increasingly operate over complete software repositories.<n> cybersecurity evaluations measure whether they can detect, reproduce, or repair vulnerabilities rather than whether they can locate the relevant code.<n>We study vulnerability localization: given a weakness class and an unfamiliar repository, identify the implementation files associated with that weakness.
arXiv Detail & Related papers (2026-09-14T17:44:51Z) - GraftyVul: Synthesising Insecure Programs Through Real-World Vulnerability Grafting [8.596028166822489]
GraftyVul constructs vulnerable programs by grafting real-world vulnerabilities into open-source projects.<n>We generate 212 verified and exploitable vulnerable programs spanning five programming languages.
arXiv Detail & Related papers (2026-08-28T05:10:49Z) - CyberFactory: Scaling Cyber Security Capabilities with Instances from the Wild [49.44069582581181]
CyberFactory transforms public vulnerability artifacts, including CVEs from the wild, into executable and verifiable task instances.<n>It uses a reusable vulnerability-analysis skill to guide the teacher through source inspection, problem solving with domain prior, and evidence-based validation.<n>On CyberGym, modelname reaches 52.4% Pass@1 under a one-hour budget, improving over its Qwen3.5 base model by +22.8 points.
arXiv Detail & Related papers (2026-08-24T12:30:10Z) - RAVEN: Agentic RAG for Automated Vulnerability Repair [48.8954115363348]
We propose RAVEN, a scalable, efficient and autonomous framework for automated vulnerability repair.<n>RAVEN integrates an agentic retrieval-augmented generation (RAG) pipeline with controlled iterative repair in a unified framework.<n>RAVEN achieves an overall repair success rate of 83.13%, outperforming all existing state-of-the-art repair frameworks.
arXiv Detail & Related papers (2026-06-21T19:18:06Z) - GCVE: A Decentralized Model for Vulnerability Identification, Publication, and Operational Enrichment [51.77969450792284]
The Global CVE initiative (G) proposes a decentralized, open, and identifier model for vulnerability identification, publication, and enrichment.<n>This paper presents G as socio-technical effort combining autonomous G Numbering Authorities, lightweight allocation rules, distributed publication, open Best Current Practices, and practical reference implementations.
arXiv Detail & Related papers (2026-05-30T19:03:39Z) - ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks? [92.21756459993695]
Exploitation is a challenging task because it requires low-level program reasoning.<n>Despite its importance and diagnostic value, exploitation remains under-evaluated.<n>We introduce ExploitGym, a large-scale, diverse, realistic benchmark on the exploitation capabilities of AI agents.
arXiv Detail & Related papers (2026-05-11T18:00:14Z) - AXE: An Agentic eXploit Engine for Confirming Zero-Day Vulnerability Reports [9.184243224127377]
Agentic eXploit Engine (AXE) is a multi-agent framework for Web application exploitation.<n>AXE achieves a 30% exploitation success rate, a 3x improvement over state-of-the-art black-box baselines.<n>For successful exploits, AXE produces actionable, reproducible proof-of-concept artifacts.
arXiv Detail & Related papers (2026-02-15T23:25:14Z) - Patch-to-PoC: A Systematic Study of Agentic LLM Systems for Linux Kernel N-Day Reproduction [27.460244103362935]
We present the first large-scale study of LLM-based Linux kernel vulnerability reproduction.<n>Using kernel security patches as input, K-Repro automates end-to-end bug reproduction of N-day vulnerabilities in the Linux kernel.<n>Our results show that K-Repro can generate PoCs that reproduce over 50% of the cases with practical time and monetary cost.
arXiv Detail & Related papers (2026-02-07T00:34:08Z) - RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories [58.32028251925354]
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area.<n>We introduce RealSec-bench, a new benchmark for secure code generation meticulously constructed from real-world, high-risk Java repositories.
arXiv Detail & Related papers (2026-01-30T08:29:01Z) - Distilling Lightweight Language Models for C/C++ Vulnerabilities [7.45549460594508]
FineSec is a novel framework that harnesses Large Language Models through knowledge distillation to enable efficient and precise vulnerability identification in C/C++s.<n>By integrating data preparation, training, evaluation, and continuous learning into a unified, single-task workflow, FineSec offers a streamlined approach.
arXiv Detail & Related papers (2025-10-08T04:58:51Z) - Automated Vulnerability Validation and Verification: A Large Language Model Approach [7.482522010482827]
This paper introduces an end-to-end multi-step pipeline leveraging generative AI, specifically large language models (LLMs)<n>Our approach extracts information from CVE disclosures in the National Vulnerability Database.<n>It augments it with external public knowledge (e.g., threat advisories, code snippets) using Retrieval-Augmented Generation (RAG)<n>The pipeline iteratively refines generated artifacts, validates attack success with test cases, and supports complex multi-container setups.
arXiv Detail & Related papers (2025-09-28T19:16:12Z) - From CVE Entries to Verifiable Exploits: An Automated Multi-Agent Framework for Reproducing CVEs [23.210122086674048]
CVE-GENIE is an automated framework designed to reproduce real-world vulnerabilities.<n>It reproduces 51% (428 of 841) CVEs published in 2024-2025, complete with their verifiable exploits, at an average cost of $2.77 per CVE.<n>Our pipeline offers a robust method to generate reproducible CVE benchmarks, valuable for diverse applications.
arXiv Detail & Related papers (2025-09-01T23:37:44Z) - A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code [49.009041488527544]
A.S.E is a repository-level evaluation benchmark for assessing the security of AI-generated code.<n>Current large language models (LLMs) still struggle with secure coding.<n>A larger reasoning budget does not necessarily lead to better code generation.
arXiv Detail & Related papers (2025-08-25T15:11:11Z) - CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scale [45.97598662617568]
We introduce CyberGym, a large-scale benchmark featuring 1,507 real-world vulnerabilities across 188 software projects.<n>We show that CyberGym leads to the discovery of 35 zero-day vulnerabilities and 17 historically incomplete patches.<n>These results underscore that CyberGym is not only a robust benchmark for measuring AI's progress in cybersecurity but also a platform for creating direct, real-world security impact.
arXiv Detail & Related papers (2025-06-03T07:35:14Z) - Autosploit: A Fully Automated Framework for Evaluating the
Exploitability of Security Vulnerabilities [47.748732208602355]
Autosploit is an automated framework for evaluating the exploitability of vulnerabilities.
It automatically tests the exploits on different configurations of the environment.
It is able to identify the system properties that affect the ability to exploit a vulnerability in both noiseless and noisy environments.
arXiv Detail & Related papers (2020-06-30T18:49:18Z)
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.