Enhancing Detection of JavaScript-WebAssembly Multilingual Malware through Static Semantics Reconstruction

Core Concepts

JWBinder, a novel program analysis technique, effectively enhances the detection of JavaScript-WebAssembly multilingual malware by capturing the interoperations between the two languages and reconstructing a unified high-level representation to characterize the malicious functionalities.

Abstract

The paper presents JWBinder, a technique designed to enhance the detection of JavaScript-WebAssembly multilingual malware (JWMM). JWMM is a new threat that hides malicious behaviors across the interoperations between JavaScript and WebAssembly, evading existing anti-virus solutions focused on monolingual code.
JWBinder works in two phases:

Language-specific data-flow analysis:

Constructs the JavaScript Program Dependency Graph (PDG) and performs a bi-directional data-flow analysis to identify the interoperations between JavaScript and WebAssembly.
Captures how JavaScript interacts with WebAssembly, such as instantiating WebAssembly instances and invoking their internal functions.

Static semantic reconstruction (SSR):

Introduces a set of abstraction rules to extract the high-level semantics of WebAssembly units and integrate them into the JavaScript PDG, creating a unified Inter-language PDG (IPDG).
Reconstructs a pure JavaScript program from the IPDG, transforming the problem of detecting multilingual malware into the detection of monolingual malware.

The evaluation shows that JWBinder effectively enhances the detection capabilities of real-world anti-virus solutions. Specifically, it increases the overall successful detection rate against JWMM from 49.1% to 86.2% on VirusTotal. JWBinder also introduces minimal side effects on benign JavaScript-WebAssembly programs, with a false positive rate of only 0.5%. Additionally, the analysis reveals that different anti-virus solutions benefit from different levels of semantic reconstruction, demonstrating the generalization ability of JWBinder.

Stats

The successful detection rate of VirusTotal on the original JWMM dataset is 49.1%.
The average number of detected engines by VirusTotal on the original JWMM dataset is 4.1.

Quotes

"The emergence of WebAssembly allows attackers to hide the malicious functionalities of JavaScript malware in cross-language interoperations, termed JavaScript-WebAssembly multilingual malware (JWMM)."
"Existing anti-virus solutions based on static program analysis are still limited to monolingual code. As a result, their detection effectiveness decreases significantly against JWMM."

Key Insights Distilled From

Static Semantics Reconstruction for Enhancing JavaScript-WebAssembly Multilingual Malware Detection

by Yifan Xia,Pi... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2310.17304.pdf

Deeper Inquiries

How can JWBinder be extended to handle dynamically generated code or data in JWMM?

JWBinder can be extended to handle dynamically generated code or data in JWMM by incorporating dynamic analysis techniques in addition to static analysis. Dynamic analysis can involve monitoring the runtime behavior of the JavaScript-WebAssembly interactions to capture any dynamically generated code or data. This can be achieved by implementing runtime instrumentation to track the execution flow and data manipulation during the interoperations between JavaScript and WebAssembly.
One approach could be to integrate dynamic taint analysis, which tracks the flow of data and identifies how it is manipulated at runtime. By combining static and dynamic analysis, JWBinder can effectively handle dynamically generated code or data in JWMM. Additionally, leveraging techniques such as symbolic execution or runtime monitoring can help capture the behavior of dynamically generated elements in the multilingual malware.

What are the potential limitations of the static analysis approach used in JWBinder, and how can they be addressed?

While static analysis is effective in capturing certain aspects of JWMM, it also has limitations that need to be addressed. Some potential limitations of the static analysis approach used in JWBinder include:

Limited visibility into runtime behavior: Static analysis may not capture the full range of behaviors exhibited by dynamically generated code or data during execution. To address this limitation, incorporating dynamic analysis techniques can provide a more comprehensive view of the runtime behavior of JWMM.

Obfuscation techniques: Malicious actors often employ obfuscation techniques to evade static analysis. JWBinder may struggle to analyze obfuscated code effectively. To overcome this limitation, integrating de-obfuscation mechanisms or enhancing the static analysis algorithms to handle obfuscated code can be beneficial.

Complexity of interoperations: The intricate interoperations between JavaScript and WebAssembly can pose challenges for static analysis. Understanding the nuances of these interactions and capturing them accurately in the analysis may be difficult. Enhancing the analysis algorithms to handle the complexity of cross-language interactions can help address this limitation.

To address these limitations, a hybrid approach combining static and dynamic analysis, along with advanced techniques like de-obfuscation and behavior analysis, can enhance the effectiveness of JWBinder in detecting and analyzing JWMM.

How can the insights from JWBinder's analysis of the interoperations between JavaScript and WebAssembly be leveraged to improve the overall security of the web ecosystem?

The insights from JWBinder's analysis of the interoperations between JavaScript and WebAssembly can be leveraged to enhance the overall security of the web ecosystem in the following ways:

Developing targeted security measures: Understanding the specific patterns and behaviors of JWMM can help in developing targeted security measures to detect and mitigate such threats effectively. By leveraging the insights from JWBinder's analysis, security solutions can be tailored to address the unique challenges posed by multilingual malware.

Enhancing detection capabilities: By incorporating the identified interoperations and malicious behaviors into security tools and systems, the overall detection capabilities can be improved. Security solutions can be updated to recognize and respond to JWMM more effectively, thereby enhancing the security posture of the web ecosystem.

Informing best practices: Insights from JWBinder's analysis can inform best practices for secure coding and development in the web ecosystem. Developers can be educated on the potential risks associated with cross-language interoperations and how to mitigate them, leading to more secure web applications and services.

By leveraging the insights from JWBinder's analysis, stakeholders in the web ecosystem can work towards a more secure and resilient environment against evolving threats like JWMM.