Chasing One-day Vulnerabilities Across Open Source Forks
- URL: http://arxiv.org/abs/2511.05097v1
- Date: Fri, 07 Nov 2025 09:25:47 GMT
- Title: Chasing One-day Vulnerabilities Across Open Source Forks
- Authors: Romain Lefeuvre, Charly Reux, Stefano Zacchiroli, Olivier Barais, Benoit Combemale,
- Abstract summary: This paper presents a novel approach to help developers identify one-day vulnerabilities in forked repositories.<n>The approach propagates vulnerability information at the commit level and performs automated impact analysis.<n>It enables automatic detection of forked projects that have not incorporated fixes, leaving them potentially vulnerable.
- Score: 3.777973175977788
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Tracking vulnerabilities inherited from third-party open-source components is a well-known challenge, often addressed by tracing the threads of dependency information. However, vulnerabilities can also propagate through forking: a repository forked after the introduction of a vulnerability, but before it is patched, may remain vulnerable in the fork well after being fixed in the original project. Current approaches for vulnerability analysis lack the commit-level granularity needed to track vulnerability introductions and fixes across forks, potentially leaving one-day vulnerabilities undetected. This paper presents a novel approach to help developers identify one-day vulnerabilities in forked repositories. Leveraging the global graph of public code, as captured by the Software Heritage archive, the approach propagates vulnerability information at the commit level and performs automated impact analysis. This enables automatic detection of forked projects that have not incorporated fixes, leaving them potentially vulnerable. Starting from 7162 repositories that, according to OSV, include vulnerable commits in their development histories, we identify 2.2 M forks, containing at least one vulnerable commit. Then we perform a strict filtering, allowing us to find 356 ___vulnerability, fork___ pairs impacting active and popular GitHub forks, we manually evaluate 65 pairs, finding 3 high-severity vulnerabilities, demonstrating the impact and applicability of this approach.
Related papers
- Malicious Agent Skills in the Wild: A Large-Scale Security Empirical Study [47.60135753021306]
Third-party agent skills extend LLM-based agents with instruction files and executable code that run on users' machines.<n>No ground-truth dataset exists to characterize the resulting threats.<n>We construct the first labeled dataset of malicious agent skills by behaviorally verifying 98,380 skills.
arXiv Detail & Related papers (2026-02-06T09:52:27Z) - Trace: Securing Smart Contract Repository Against Access Control Vulnerability [58.02691083789239]
GitHub hosts numerous smart contract repositories containing source code, documentation, and configuration files.<n>Third-party developers often reference, reuse, or fork code from these repositories during custom development.<n>Existing tools for detecting smart contract vulnerabilities are limited in their ability to handle complex repositories.
arXiv Detail & Related papers (2025-10-22T05:18:28Z) - What Do They Fix? LLM-Aided Categorization of Security Patches for Critical Memory Bugs [46.325755802511026]
We developLM, a dual-method pipeline that integrates two approaches based on a Large Language Model (LLM) and a fine-tuned small language model.<n>LM successfully identified 111 of 5,140 recent Linux kernel patches addressing OOB or UAF vulnerabilities, with 90 true positives confirmed by manual verification.
arXiv Detail & Related papers (2025-09-26T18:06:36Z) - Weakly Supervised Vulnerability Localization via Multiple Instance Learning [46.980136742826836]
We propose a novel approach called WAVES for WeAkly supervised Vulnerability localization via multiplE inStance learning.<n>WAVES has the capability to determine whether a function is vulnerable (i.e., vulnerability detection) and pinpoint the vulnerable statements.<n>Our approach achieves comparable performance in vulnerability detection and state-of-the-art performance in statement-level vulnerability localization.
arXiv Detail & Related papers (2025-09-14T15:11:39Z) - Decompiling Smart Contracts with a Large Language Model [51.49197239479266]
Despite Etherscan's 78,047,845 smart contracts deployed on (as of May 26, 2025), a mere 767,520 ( 1%) are open source.<n>This opacity necessitates the automated semantic analysis of on-chain smart contract bytecode.<n>We introduce a pioneering decompilation pipeline that transforms bytecode into human-readable and semantically faithful Solidity code.
arXiv Detail & Related papers (2025-06-24T13:42:59Z) - PoCGen: Generating Proof-of-Concept Exploits for Vulnerabilities in Npm Packages [13.877936187495555]
We present PoCGen, a novel approach to autonomously generate and validate PoC exploits for vulnerabilities in npm packages.<n>PoCGen successfully generates exploits for 77% of the vulnerabilities in the SecBench$.$js dataset.
arXiv Detail & Related papers (2025-06-05T12:37:33Z) - Eradicating the Unseen: Detecting, Exploiting, and Remediating a Path Traversal Vulnerability across GitHub [1.2124551005857036]
Vulnerabilities in open-source software can cause cascading effects in the modern digital ecosystem.<n>We identified 1,756 vulnerable open-source projects, some of which are very influential.<n>We have responsibly disclosed the vulnerability to the maintainers, and 14% of the reported vulnerabilities have been remediated.
arXiv Detail & Related papers (2025-05-26T16:29:21Z) - Discovery of Timeline and Crowd Reaction of Software Vulnerability Disclosures [47.435076500269545]
Apache Log4J was found to be vulnerable to remote code execution attacks.
More than 35,000 packages were forced to update their Log4J libraries with the latest version.
It is practically reasonable for software developers to update their third-party libraries whenever the software vendors have released a vulnerable-free version.
arXiv Detail & Related papers (2024-11-12T01:55:51Z) - Detecting Security Patches via Behavioral Data in Code Repositories [11.052678122289871]
We show a system to automatically identify security patches using only the developer behavior in the Git repository.
We showed we can reveal concealed security patches with an accuracy of 88.3% and F1 Score of 89.8%.
arXiv Detail & Related papers (2023-02-04T06:43:07Z) - VulCurator: A Vulnerability-Fixing Commit Detector [8.32137934421055]
VulCurator is a tool that leverages deep learning on richer sources of information.
VulCurator outperforms the state-of-the-art baselines up to 16.1% in terms of F1-score.
arXiv Detail & Related papers (2022-09-07T16:11:31Z) - Automated Mapping of Vulnerability Advisories onto their Fix Commits in
Open Source Repositories [7.629717457706326]
We present an approach that combines practical experience and machine-learning (ML)
An advisory record containing key information about a vulnerability is extracted from an advisory.
A subset of candidate fix commits is obtained from the source code repository of the affected project.
arXiv Detail & Related papers (2021-03-24T17:50:35Z)
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.