Full Report
“Operating system facilities, such as the kernel and utility programs, are typically assumed to be reliable. In our recent experiments, we have been able to crash 25-33% of the utility programs on any version of UNIX that was tested.” [1] Those were the original words in one of the first fuzzing studies where Prof. Barton Miller was first to use the term ‘fuzzing’ One can see the importance of fuzzing as one of the techniques used to test software security against malformed input leading to crashes and in some cases exploitable bugs.
Analysis Summary
# Tool/Technique: Wadi Fuzzer
## Overview
Wadi is a grammar-based fuzzer specifically designed for testing web browsers. It leverages W3C standards (like DOM, Web API, and Web IDL grammars) to systematically generate malformed web content test cases intended to break browser functionality, leading to crashes or exploitable bugs. It acts as a module for the NodeFuzz fuzzing harness and utilizes AddressSanitizer (ASan) for instrumentation on target systems.
## Technical Details
- Type: Tool (Fuzzer)
- Platform: Linux, macOS (utilizes ASan integration)
- Capabilities: Grammar-based test case generation focused on web content standards (DOM, Web API, Web IDL); integrates with NodeFuzz harness for execution and monitoring; uses AddressSanitizer for memory error detection.
- First Seen: Introduced at Defcon 23 (October 2015).
## MITRE ATT&CK Mapping
Fuzzing in general falls under techniques used during the initial phases of development or targeted security research, often mapping to the **Resource Development** tactic if used proactively, or **Triage/Investigation** if used against deployed software to find new flaws. As a testing tool, direct execution mapping is less common unless specifically used against a target to gain initial access.
- **TA0042 - Resource Development** (If used for proactive vulnerability research)
- **T1595 - Active Scanning** (Conceptual overlap, as it probes software boundaries)
- T1595.002 - Internet Scan (If used broadly against web services)
*(Note: Direct mapping to TTPs for vulnerability discovery tools is often abstract, as fuzzers primarily aid in **Discovery** phase techniques rather than active execution TTPs against a victim.)*
## Functionality
### Core Capabilities
- **Grammar-Based Generation:** Creates test cases based on grammars describing web standards (DOM, Web API, Web IDL) to construct semi-valid, yet malicious, inputs for browsers.
- **Integration with Fuzzing Harness:** Functions as a module for NodeFuzz, which handles the loading of the target application, feeding it test cases, monitoring for crashes, saving crash-inducing inputs, and restarting the target.
- **Memory Error Detection:** Relies on Google's AddressSanitizer (ASan) instrumentation (compiled into the target browser, e.g., Firefox/Chrome) to precisely detect and report memory corruption errors upon application crash.
### Advanced Features
- **W3C Standard Utilization:** Specifically targets W3C specifications (DOM, Web IDL) to ensure test cases closely mirror valid structures while introducing malformation within those boundaries.
- **Test Case Construction:** Involves complex steps like creating internal callbacks, inserting element creation blocks, generating JavaScript statements based on fuzzing functions, and ensuring the output script is structured according to the grammar.
## Indicators of Compromise
As a testing/fuzzing tool designed to discover vulnerabilities rather than execute post-exploitation commands, it does not typically generate standard IOCs like C2 traffic or persistence mechanisms. IOCs relate to the tool's setup:
- File Hashes: Not specified in the article.
- File Names: `WADI-Module.js`, `randoms.js` (Wadi specific files).
- Registry Keys: N/A
- Network Indicators: N/A
- Behavioral Indicators: Inputting highly malformed HTML/JavaScript structures into a target web browser process. Monitoring for unexpected crashes or memory corruption errors reported by ASan in debug logs.
## Associated Threat Actors
No specific threat actors are associated with the *use* of Wadi in malicious campaigns, as it is presented as a general security research and vulnerability discovery tool.
## Detection Methods
Detection focuses on the analysis environment and the underlying memory instrumentation:
- **Signature-based detection:** Signatures for the specific configuration files or scripts used to run NodeFuzz with Wadi (e.g., modified `config.js`).
- **Behavioral detection:** Detecting the high volume of memory allocation/deallocation attempts and abnormal process termination patterns typical of a fuzzer running against a browser application.
- **YARA rules if available:** No specific YARA rules are provided in the text.
## Mitigation Strategies
Mitigations are general strategies for hardening software against fuzzing-discovered vulnerabilities:
- **Prevention measures:** Utilizing compiler-level security features and memory instrumentation (like ASan, PageHeap) during development builds to identify and fix memory safety issues early.
- **Hardening recommendations:** Ensuring all web content rendering engines and APIs are patched against newly discovered vulnerabilities (e.g., those found in Chromium). Employing sandboxing and least privilege designs for browser processes.
## Related Tools/Techniques
- **Fuzzing Methodology:** Random generation (Miller's original approach), Syntax testing (Boris Beizer).
- **Fuzzer Harnesses:** NodeFuzz (Wadi integrates with this), Grinder (Windows equivalent).
- **Memory Error Detectors:** PageHeap (Windows), AddressSanitizer (ASan - Linux/macOS).
- **Related Tools:** Black-box testing tools mentioned in references.