New ODGen Tool Unearths 180 Zero-Days in Node.js Libraries

Researchers at Johns Hopkins University recently uncovered a startling 180 zero-day vulnerabilities across thousands of Node.js libraries using a new code analysis tool they developed specifically for the purpose, called ODGen.

Seventy of those flaws have since received common vulnerabilities and exposures (CVE) identifiers. They include command injection flaws, path traversal vulnerabilities, arbitrary code execution issues, and cross-site scripting vulnerabilities — some of them in widely used applications.

In a paper released at the Usenix Security Symposium earlier this month, the Johns Hopkins researchers — Song Li, Mingqing Kang, Jianwei Hou, and Yinzhi Cao — described ODGen as a better alternative to current code-analysis and so-called graph query-based approaches for finding Node.js vulnerabilities.

Program analysis-based approaches have proved useful in helping detect individual vulnerability types such as code-injection flaws in JavaScript. But they cannot be easily extended to detect all kind of vulnerabilities that might be present in the Node.js platform, the researchers said. Similarly, graph-based code-analysis methods — where code is first represented as a graph and then queried for specific coding errors — works well in environments such as C++ and PHP. However, graph-based approaches are not as efficient in mining for JavaScript vulnerabilities because of the programming language’s extensive use of dynamic features, they noted.

A ‘Novel’ Approach for Finding JavaScript Vulnerabilities

So, the researchers instead developed what they described as a “novel” and better method called Object Dependence Graph (ODG) that can be used for detecting Node.js vulnerabilities. They implemented ODGen to generate “ODG” for Node.js programs to detect vulnerabilities, they said.

Cao, assistant professor of computer science at Johns Hopkins University and a co-author of the research report, uses a couple of analogies to describe graph-based code analysis in general and their proposed Objective Dependence Graph. “If we consider a vulnerability as a special pattern — say, a green node connected with a red node and then a black node — a graph-based code-analysis tool first converts programs to a graph with many nodes and edges,” Cao says. “Then the tool looks for such patterns in the graph to locate a vulnerability.”

The Object Dependence Graph that the researchers have proposed refines this approach by representing JavaScript objects as nodes and adding features — including dependencies between objects — that are specific to the programming language, and then querying for errors. Cao describes how the method works using grains in a handful of rice: If all the grains look the same before boiling but assume two different shades after boiling — one representing good grains and the other bad grains — then it becomes easier to spot and weed out the bad grains. “Abstract interpretation is kind of like the boiling process that converts rice — that is, programs — into different colored objects” so errors are easier to spot, Cao says.

A Variety of Bugs

To see if their approach works, the researchers first tested ODGen against a sample of 330 previously reported vulnerabilities in Node.js packages on the node package manager (npm) repository. The test showed the scanner correctly identifying 302 of the 330 vulnerabilities. Buoyed by the relatively high accuracy rate, the researchers ran ODGen against some 300,000 Java packages in npm. The scanner reported a total of 2,964 potential vulnerabilities across the packages. The researchers checked 264 of them — all with more than 1,000 downloads per week on average — and were able to confirm 180 as being legitimate vulnerabilities. Forty-three of them were at the application level, 122 were in packages that are imported by other applications or code, and the remaining 15 were present in indirect packages.

A plurality (80) of the confirmed vulnerabilities that ODGen detected were command injection flows that allow attackers to execute arbitrary code at the operating system level via a vulnerable application. Thirty were path traversal flaws; 24 enabled code tampering, and 19 involved a specific type of command injection attack called prototype pollution.