New Study Uncovers Vulnerable Code Pattern Exposes GitHub Projects To Path Traversal Attacks

A comprehensive security research study has revealed a widespread vulnerable code pattern affecting thousands of open-source projects on GitHub, exposing them to critical path traversal attacks that could allow malicious actors to access sensitive files and crash server systems. The vulnerability, classified as CWE-22, enables attackers to bypass intended directory restrictions and access files outside […] The post New Study Uncovers Vulnerable Code Pattern Exposes GitHub Projects To Path Traversal Attacks appeared first on Cyber Security News.

Jun 2, 2025 - 13:40

New Study Uncovers Vulnerable Code Pattern Exposes GitHub Projects To Path Traversal Attacks

A comprehensive security research study has revealed a widespread vulnerable code pattern affecting thousands of open-source projects on GitHub, exposing them to critical path traversal attacks that could allow malicious actors to access sensitive files and crash server systems.

The vulnerability, classified as CWE-22, enables attackers to bypass intended directory restrictions and access files outside the designated public directories, potentially compromising system confidentiality, integrity, and availability.

The vulnerable pattern centers around a seemingly innocuous JavaScript code snippet that creates a simple static file server using Node.js.

The problematic code uses the path.join() function to combine a current working directory with user-supplied pathname input without proper validation, creating a gateway for attackers to traverse the file system.

When exploitation occurs, malicious users can craft specially designed URLs containing directory traversal sequences like ../../etc/passwd to access critical system files, or target large files such as /dev/urandom to trigger denial-of-service attacks by exhausting server memory.

Research conducted by Jafar Akhoundali, Hamidreza Hamidi, Kristian Rietveld, and Olga Gadyatskaya from LIACS, Leiden University and Technical and Vocational University Mashhad identified this pattern as particularly dangerous due to its widespread adoption across the development community.

Their automated analysis pipeline discovered 1,756 exploitable instances of this vulnerability across GitHub projects, with affected repositories ranging from small personal projects to popular open-source initiatives with thousands of stars.

The median CVSS score for confirmed vulnerabilities reached 9.1, indicating high severity impact.

The researchers’ investigation traced the vulnerability’s origins to code snippets that first emerged around 2010 on developer platforms including GitHub Gist and StackOverflow.

Despite multiple developers raising security concerns over the years, the vulnerable pattern continued to propagate due to widespread copy-paste practices and a fundamental misunderstanding about the code’s security implications.

The persistence of this pattern highlights a critical gap in security awareness within the development community, particularly regarding proper input validation and path handling.

The Infection Mechanism: How Vulnerable Code Spreads

The study revealed a troubling propagation mechanism that extends far beyond simple code reuse. The vulnerable pattern has infiltrated popular Large Language Models (LLMs), creating a self-perpetuating cycle of insecure code generation.

Overall flowchart of the proposed pipeline (Source – Arxiv)

When researchers tested prominent AI coding assistants including GPT-3.5, GPT-4, Copilot, Claude, and Gemini, they found that these models consistently generated vulnerable code even when explicitly instructed to create secure implementations.

const filename = path.join(process.cwd(), uri); // Vulnerable line
const fileContent = fs.readFileSync(filename);
response. Write(fileContent);

This contamination represents a paradigm shift in how vulnerabilities spread through the software ecosystem.

While traditional security concerns focused on direct code copying from platforms like StackOverflow, the integration of vulnerable patterns into AI training datasets means that developers using AI-assisted coding tools unknowingly introduce these security flaws into new projects.

The researchers found that 70% of LLM-generated code samples contained the vulnerable pattern when asked to create secure static file servers, demonstrating the depth of this contamination.

The automation pipeline developed by the research team successfully generated patches for 1,600 of the discovered vulnerabilities using GPT-4, though only 63 projects ultimately implemented fixes after responsible disclosure efforts.

This low remediation rate underscores the challenges in scaling vulnerability management across the open-source ecosystem, highlighting the urgent need for improved security awareness and automated remediation tools to protect against such widespread code pattern vulnerabilities.

Celebrate 9 years of ANY.RUN! Unlock the full power of TI Lookup plan (100/300/600/1,000+ search requests), and your request quota will double.

The post New Study Uncovers Vulnerable Code Pattern Exposes GitHub Projects To Path Traversal Attacks appeared first on Cyber Security News.