I am a second year Ph.D. student at Purdue University, advised by Professor Xiangyu Zhang. I previously obtained my B.Sc. degree with Zhiyuan Honours from Shanghai Jiao Tong University (SJTU) in 2018.
I am a second year Ph.D. student at Purdue University, advised by Professor Xiangyu Zhang. I previously obtained my B.Sc. degree with Zhiyuan Honours from Shanghai Jiao Tong University (SJTU) in 2018. I am also a core member of the CTF team 0ops. Sometimes I play with A*0*E, too. Click to view my up-to-date CV.
My research interest mainly lies in software engineering and program analysis, especially for native code without sources.
$ curl -s https://www.cs.purdue.edu/homes/zhan3299/img/zhuo.png | strings | tail -n 2
Malware Analysis, Forced Execution, Probabilistic Program Analysis
Malware is a prominent security threat and exposing malware behavior is a critical challenge. Recent malware often has payload that is only released when certain conditions are satisfied. It is hence difficult to fully disclose the payload by simply executing the malware. In addition, malware samples may be equipped with cloaking techniques such as VM detectors that stop execution once detecting that the malware is being monitored. Forced execution is a highly effective method to penetrate malware self-protection and expose hidden behavior, by forcefully setting certain branch outcomes. However, an existing state-of-the-art forced execution technique X-Force is very heavy-weight, requiring tracing individual instructions, reasoning about pointer alias relations on-the-fly, and repairing invalid pointers by on demand memory allocation.
We develop a light-weight and practical forced execution technique. Without losing analysis precision, it avoids tracking individual instructions and on demand allocation. Under our scheme, a forced execution is very similar to a native one. It features a novel memory pre-planning phase that pre-allocates a large memory buffer, and then initializes the buffer, and variables in the subject binary, with carefully crafted values in a random fashion before the real execution. The pre-planning is designed in such a way that dereferencing an invalid pointer has a very large chance to fall into the pre-allocated region and hence does not cause any exception, and semantically unrelated invalid pointer dereferences highly likely access disjoint (pre-allocated) memory regions, avoiding state corruptions with probabilistic guarantees. Our experiments show that our technique is 84 times faster than X-Force, has 6.5X and 10% fewer false positives and negatives for program dependence detection, respectively, and can expose 98% more malicious behaviors in 400 recent malware samples.
Path Sampling, Abstract Interpretation, Binary Analysis, Data Dependence
Binary program dependence analysis determines dependence between instructions and hence is important for many applications that have to deal with executables without any symbol information. A key challenge is to identify if multiple memory read/write instructions access the same memory location. The state-of-the-art solution is the value set analysis (VSA) that uses abstract interpretation to determine the set of addresses that are possibly accessed by memory instructions. However, VSA is conservative and hence leads to a large number of bogus dependences and then substantial false positives in downstream analyses such as malware behavior analysis. Furthermore, existing public VSA implementations have difficulty scaling to complex binaries.
In this paper, we propose a new binary dependence analysis called BDA enabled by a randomized abstract interpretation technique. It features a novel whole program path sampling algorithm that is not biased by path length, and a per-path abstract interpretation avoiding precision loss caused by merging paths in traditional analyses. It also provides probabilistic guarantees. Our evaluation on SPECINT2000 programs shows that it can handle complex binaries such as gcc whereas VSA implementations from the-state-of-art platforms have difficulty producing results for many SPEC binaries. In addition, the dependences reported by BDA are 75 and 6 times smaller than Alto, a scalable binary dependence analysis tool, and VSA, respectively, with only 0.19% of true dependences observed during dynamic execution missed (by BDA). Applying BDA to call graph generation and malware analysis shows that BDA substantially supersedes the commercial tool IDA in recovering indirect call targets and outperforms a state-of-the-art malware analysis tool Cuckoo by disclosing 3 times more hidden payloads.
Disassembly, Binary Rewrite, Probabilistic Program Analysis
Disassembling stripped binaries is a prominent challenge for binary analysis, due to the interleaving of code segments and data, and the difficulties of resolving control transfer targets of indirect calls and jumps. As a result, most existing disassemblers have both false positives (FP) and false negatives (FN). We observe that uncertainty is inevitable in disassembly due to the information loss during compilation and code generation. Therefore, we propose to model such uncertainty using probabilities and propose a novel disassembly technique, which computes a probability for each address in the code space, indicating its likelihood of being a true positive instruction. The probability is computed from a set of features that are reachable to an address, including control flow and data flow features. Our experiments with more than two thousands binaries show that our technique does not have any FN and has only 3.7% FP. In comparison, a state-of-the-art superset disassembly technique has 85% FP. A rewriter built on our disassembly can generate binaries that are only half of the size of those by superset disassembly and run 3% faster. While many widely-used disassemblers such as IDA and BAP suffer from missing function entries, our experiment also shows that even without any function entry information, our disassembler can still achieve 0 FN and 6.8% FP.