PatchFuzz: Patch Fuzzing for JavaScript Engines (2505.00289v1)

Abstract: Patch fuzzing is a technique aimed at identifying vulnerabilities that arise from newly patched code. While researchers have made efforts to apply patch fuzzing to testing JavaScript engines with considerable success, these efforts have been limited to using ordinary test cases or publicly available vulnerability PoCs (Proof of Concepts) as seeds, and the sustainability of these approaches is hindered by the challenges associated with automating the PoC collection. To address these limitations, we propose an end-to-end sustainable approach for JavaScript engine patch fuzzing, named PatchFuzz. It automates the collection of PoCs of a broader range of historical vulnerabilities and leverages both the PoCs and their corresponding patches to uncover new vulnerabilities more effectively. PatchFuzz starts by recognizing git commits which intend to fix security bugs. Subsequently, it extracts and processes PoCs from these commits to form the seeds for fuzzing, while utilizing code revisions to focus limited fuzzing resources on the more vulnerable code areas through selective instrumentation. The mutation strategy of PatchFuzz is also optimized to maximize the potential of the PoCs. Experimental results demonstrate the effectiveness of PatchFuzz. Notably, 54 bugs across six popular JavaScript engines have been exposed and a total of $62,500 bounties has been received.

An Analytical Overview of PatchFuzz: Fuzzing JavaScript Engine Patches

This essay provides an examination of "PatchFuzz: Patch Fuzzing for JavaScript Engines," focusing on its approach, results, and implications for detecting vulnerabilities in JavaScript engines. The research presented outlines a systematic method for identifying and leveraging security patches to improve the detection of vulnerabilities that may have been overlooked or introduced during patching.

Overview of PatchFuzz Methodology

PatchFuzz employs a comprehensive strategy to address the challenges in fuzzing JavaScript engines, specifically targeting vulnerabilities arising from newly patched code. The core of this research is a process termed "patch fuzzing," which begins with identifying patches intended to fix security bugs in JavaScript engines. PatchFuzz automates the collection and processing of Proofs of Concept (PoCs) from these commits, recognizing their potential to divulge hidden vulnerabilities.

The methodology includes several innovative components:

1. Security Patch Recognition: PatchFuzz introduces a rule-based approach to classify security patches using commit messages, keywords, and code changes. This approach circumvents the limitations posed by machine learning models in accurately identifying security-focused patches within JavaScript engine commits.
2. Executable PoC Extraction: A significant portion of PatchFuzz involves processing extracted PoCs to eliminate grammar errors, allowing for seamless execution. This is achieved through a carefully curated list of error patterns specific to JavaScript engines.
3. Selective Instrumentation: PatchFuzz focuses fuzzing efforts on "hot code" — areas frequently patched due to vulnerabilities. By constructing call graphs and selectively instrumenting these areas, PatchFuzz maximizes its effectiveness in exploring code most susceptible to security flaws.
4. Adaptive Mutation Strategy: Mutations are performed at both expression and statement levels, ensuring integrity and explorability of PoCs. This careful balance aids in uncovering vulnerabilities without compromising the original semantics of the PoCs.

Empirical Results and Implications

PatchFuzz's effectiveness is demonstrated through its performance on six major JavaScript engines: JavaScriptCore, V8, SpiderMonkey, ChakraCore, JerryScript, and QuickJS. The experimental results are notable: PatchFuzz successfully identified 54 bugs across these engines, receiving \$62,500 in bug bounties and assigning 25 CVE IDs. This outcome is a substantial improvement over existing fuzzers like Superion, DIE, and FuzzJIT.

Furthermore, PatchFuzz's selective instrumentation strategy significantly enhances code coverage analysis, ensuring that resources are optimally allocated to areas most likely to harbor vulnerabilities. This focused approach helps not only in identifying new vulnerabilities but also in improving patch efficacy by providing targeted insights into potentially flawed patches.

Implications for Future AI Developments

PatchFuzz underscores the importance of agile and thorough testing methodologies in the field of software security. The success of PatchFuzz suggests several promising avenues for future research and development:

• Patch-Oriented Security Techniques: The constructive feedback loop between patch application and fuzzing illustrated by PatchFuzz could inspire new tools and methodologies centered on real-time patch verification and adjustment.
• Automated Analysis: Investigating ways to automate the rule-based identification of patches, as achieved manually in PatchFuzz, can lead to faster and more efficient detection processes.
• Expanding Applicability: While PatchFuzz is applied to JavaScript engines, its principles can extend to other software domains, enhancing security across open-source and proprietary software landscapes.

The PatchFuzz approach not only proves effective but also exemplifies a critical step forward in automated security patch testing. As AI and automation become increasingly central to software development, tools like PatchFuzz highlight how targeted, intelligent testing can dramatically improve both patch efficacy and overall software security.