Date of Award

December 2017

Degree Type


Degree Name

Doctor of Philosophy (PhD)


Electrical Engineering and Computer Science


Heng Yin


Cybersecurity, Exploit Diagnosis, Exploit Mitigation, Forced Execution, Malicious JavaScript

Subject Categories



Malicious JavaScript has become an important attack vector for software exploitation attacks and imposes a severe threat to computer security. In particular, three major class of problems, malware detection, exploit diagnosis, and exploits mitigation, bring considerable challenges to security researchers. Although a lot of research efforts have been made to address these threats, they have fundamental limitations and thus cannot solve the problems.

Existing analysis techniques fall into two general categories: static analysis and dynamic analysis. Static analysis tends to produce inaccurate results (both false positive and false negative) and is vulnerable to a wide series of obfuscation techniques. Thus, dynamic analysis is constantly gaining popularity for exposing the typical features of malicious JavaScript. However, existing dynamic analysis techniques possess limitations such as limited code coverage and incomplete environment setup, leaving a broad attack surface for evading the detection.

Once a zero-day exploit is captured, it is critical to quickly pinpoint the JavaScript statements that uniquely characterize the exploit and the payload location in the exploit. However, the current diagnosis techniques are inadequate because they approach the problem either from a

JavaScript perspective and fail to account for “implicit” data flow invisible at JavaScript level, or from a binary execution perspective and fail to present the JavaScript level view of exploit.

Although software vendors have deployed techniques like ASLR, sandbox, etc. to mitigate JavaScript exploits, hacking contests (e.g.,PWN2OWN, GeekPWN) have demonstrated that the latest software (e.g., Chrome, IE, Edge, Safari) can still be exploited. An ideal JavaScript exploit mitigation solution should be flexible and allow for deployment without requiring code changes. To combat malicious JavaScript, this dissertation addresses these problems through enriched executions, which explore arbitrary paths for detection, preserve JS-binary semantics for diagnosis, and perturbs memory with chaff code for mitigation.

Firstly, JSForce, a forced execution engine for JavaScript, is proposed and developed to improve the detection results of current malicious JavaScript detection techniques. It drives an arbitrary JavaScript snippet to execute along different paths without any input or environment

setup. While increasing code coverage, JSForce can tolerate invalid object accesses while introducing no runtime errors during execution.

Secondly, JScalpel, a system that utilizes the JavaScript context information from the JavaScript level to perform context-aware binary analysis, is presented for JavaScript exploit diagnosis. In essence, it performs JS-Binary analysis to (1) generate a minimized exploit script,

which in turn helps to generate a signature for the exploit, and (2) precisely locate the payload within the exploit. It replaces the malicious payload with a friendly payload and generates a PoV for the exploit.

Thirdly, ChaffyScript, a vulnerability-agnostic mitigation system, is introduced to block JavaScript exploits via undermining the memory preparation stage. Specifically, given suspicious JavaScript, ChaffyScript rewrites the code to insert memory perturbation code, and then generates semantically-equivalent code. JavaScript exploits will fail as a result of unexpected memory states introduced by memory perturbation code, while the benign JavaScript still behaves as expected since the memory perturbation code does not change the JavaScript’s original semantics.


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