I am a postdoctoral researcher at Purdue University, advised by Samuel Conte Professor Xiangyu Zhang. I completed my Ph.D. at Purdue University in 2023 and previously obtained my B.Sc. with Zhiyuan Honours from Shanghai Jiao Tong University (SJTU) in 2018.
I am passionate about hardcore hacking and currently active in Web3 security with Offside Labs. Before this, I was a core member of CTF teams 0ops and A*0*E. My efforts in securing the digital world have earned me over $?00,000 in bug bounties.
I am a postdoctoral researcher at Purdue University, advised by Samuel Conte Professor Xiangyu Zhang. I completed my Ph.D. at Purdue University in 2023 and previously obtained my B.Sc. with Zhiyuan Honours from Shanghai Jiao Tong University (SJTU) in 2018. I am passionate about hardcore hacking and currently active in Web3 security with Offside Labs. Before this, I was a core member of CTF teams 0ops and A*0*E. My efforts in securing the digital world have earned me over $?00,000 in bug bounties.
My research interest mainly lies in software engineering and program analysis, especially for native code without sources.
I also draw my attention to Web3 security, working on automated bug detection and the identification of MEV strategies in smart contracts.
Run the following command in a terminal (GNU):
$ echo "$(echo "ghNsgnm" | md5sum - | xxd -r -p | base64 | cut -c3-10)@purdue.edu"
abstract ➲
Keywords:
Decompilation, Probabilistic Analysis
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Abstract:
Binary analysis, a cornerstone technique in cybersecurity, enables the examination of binary executables, irrespective of source code availability. It plays a critical role in understanding program behaviors, detecting software bugs, and mitigating potential vulnerabilities, specially in situations where the source code remains out of reach. However, aligning the efficacy of binary analysis with that of source-level analysis remains a significant challenge, primarily due to the uncertainty caused by the loss of semantic information during the compilation process.
This dissertation presents an innovative probabilistic approach, termed as probabilistic binary analysis, designed to combat the intrinsic uncertainty in binary analysis. It builds on the fundamental principles of program sampling and probabilistic inference, enhanced further by an iterative refinement architecture. The dissertation suggests that a thorough and practical method of sampling program behaviors can yield a substantial quantity of hints which could be instrumental in recovering lost information, despite the potential inclusion of some inaccuracies. Consequently, a probabilistic inference technique is applied to systematically incorporate and process the collected hints, suppressing the incorrect ones, thereby enabling the interpretation of high-level semantics. Furthermore, an iterative refinement mechanism is deployed to augment the efficiency of the probabilistic analysis in subsequent applications, facilitating the progressive enhancement of analysis outcomes through an automated or human-guided feedback loop.
This work offers an in-depth understanding of the challenges and solutions related to assessing low-level program representations and systematically handling the inherent uncertainty in binary analysis. It aims to contribute to the field by advancing the development of precise, reliable, and interpretable binary analysis solutions, thereby setting the groundwork for future exploration in this domain.
abstract ➲
Keywords:
Path Sampling, Abstract Interpretation, Binary Analysis, Data Dependence
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Abstract:
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.
abstract ➲
Keywords:
Binary Analysis, Variable Recovery, Probabilistic Analysis, Reverse Engineering
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Abstract:
Recovering variables and data structure information from stripped binary is a prominent challenge in binary program analysis. While various state-of-the-art techniques are effective in specific settings, such effectiveness may not generalize. This is mainly because the problem is inherently uncertain due to the information loss in compilation. Most existing techniques are deterministic and lack a systematic way of handling such uncertainty. We propose a novel probabilistic technique for variable and structure recovery. Random variables are introduced to denote the likelihood of an abstract memory location having various types and structural properties such as being a field of some data structure. These random variables are connected through probabilistic constraints derived through program analysis. Solving these constraints produces the posterior probabilities of the random variables, which essentially denote the recovery results. Our experiments show that our technique substantially outperforms a number of state-of-the-art systems, including IDA, Ghidra, Angr, and Howard. Our case studies demonstrate the recovered information improves binary code hardening and binary decompilation.
abstract ➲
Keywords:
Fuzz, Binary Rewriting, Probabilistic Analysis
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Abstract:
Fuzzing stripped binaries poses many hard challenges as fuzzers require instrumenting binaries to collect runtime feedback for guiding input mutation. However, due to the lack of symbol information, correct instrumentation is difficult on stripped binaries. Existing techniques either rely on hardware and expensive dynamic binary translation engines such as QEMU, or make impractical assumptions such as binaries do not have inlined data. We observe that fuzzing is a highly repetitive procedure providing a large number of trial-and-error opportunities. As such, we propose a novel incremental and stochastic rewriting technique STOCHFUZZ that piggy-backs on the fuzzing procedure. It generates many different versions of rewritten binaries whose validity can be approved/disapproved by numerous fuzzing runs. Probabilistic analysis is used to aggregate evidence collected through the sample runs and improve rewriting. The process eventually converges on a correctly rewritten binary. We evaluate STOCHFUZZ on two sets of real-world programs and compare with five other baselines. The results show that STOCHFUZZ outperforms state-of-the-art binary-only fuzzers (e.g., e9patch, ddisasm, and RetroWrite) in terms of soundness and cost-effectiveness and achieves performance comparable to source-based fuzzers. STOCHFUZZ is publicly available.
abstract ➲
Keywords:
Binary Analysis, Deep Learning Security, Probabilistic Analysis
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Abstract:
Deep Learning (DL) models are increasingly used in many cyber-security applications and achieve superior performance compared to traditional solutions. In this paper, we study backdoor vulnerabilities in naturally trained models used in binary analysis. These backdoors are not injected by attackers but rather products of defects in datasets and/or training processes. The attacker can exploit these vulnerabilities by injecting some small fixed input pattern (e.g., an instruction) called backdoor trigger to their input (e.g., a binary code snippet for a malware detection DL model) such that misclassification can be induced (e.g., the malware evades the detection). We focus on transformer models used in binary analysis. Given a model, we leverage a trigger inversion technique particularly designed for these models to derive trigger instructions that can induce misclassification. During attack, we utilize a novel trigger injection technique to insert the trigger instruction(s) to the input binary code snippet. The injection makes sure that the code snippets' original program semantics are preserved and the trigger becomes an integral part of such semantics and hence cannot be easily eliminated. We evaluate our prototype PELICAN on 5 binary analysis tasks and 15 models. The results show that PELICAN can effectively induce misclassification on all the evaluated models in both white-box and black-box scenarios. Our case studies demonstrate that PELICAN can exploit the backdoor vulnerabilities of two closed-source commercial tools.
Keywords:
Binary Analysis, Type Inference, Variable Recovery, Large Language Model
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Abstract:
Decompilation aims to recover the source code form of a binary executable and hence has a wide range of applications in cyber security, such as malware analysis and legacy code hardening. A prominent challenge is to recover variables, including both primitive and complex variables such as user-defined data structures, and their symbol information, including names and types. Existing efforts focus on solving parts of the problem, e.g., recovering only types (without names) or only local variables (without user-defined structures). In this paper, we propose \tool, a novel LLM-based technique to recover both names and types for local variables and structures including user-defined structures. It features fine-tuning two LLMs to handle local variables and structures, respectively. To overcome the input token limitations of existing LLMs, we also devise a novel Prolog-based algorithm to aggregate and cross-check results from multiple LLM queries, suppressing uncertainty and hallucinations. Our experiments show that \tool is effective in recovering variable information and user-defined data structures, substantially outperforming the state-of-the-art methods OSPREY and DIRTY.
Keywords:
Binary Analysis, Variable Name Recovery, Large Language Model
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Abstract:
Decompilation aims to recover the source code form of a binary executable. It has many applications in security and software engineering such as malware analysis, vulnerability detection and code reuse. A prominent challenge in decompilation is to recover variable names. We propose a novel method that leverages the synergy of large language model (LLM) and program analysis. Language models encode rich multi-modal knowledge, but its limited input size prevents providing sufficient global context for name recovery. We propose to divide the task to many LLM queries and use program analysis to correlate and propagate the query results, which in turn improves the performance of LLM by providing additional contextual information. Our results show that 75% of the recovered names are considered good by users and our technique outperforms the state-of-the-art technique by 16.5% and 20.23% in precision and recall, respectively.
Keywords:
Smart Contract, Web3 Security, Blockchain
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Abstract:
Exploitable bugs in smart contracts have caused significant monetary loss. Despite the substantial advances in smart contract bug finding, exploitable bugs and real-world attacks are still trending. In this paper we systematically investigate 516 unique real-world smart contract vulnerabilities in years 2021-2022, and study how many can be exploited by malicious users and cannot be detected by existing analysis tools. We further categorize the bugs that cannot be detected by existing tools into seven types and study their root causes, distributions, difficulties to audit, consequences, and repair strategies. For each type, we abstract them to a bug model (if possible), facilitating finding similar bugs in other contracts and future automation. We leverage the findings in auditing real world smart contracts, and so far we have been rewarded with $102,660 bug bounties for identifying 15 critical zero-day exploitable bugs, which could have caused up to $22.52 millions monetary loss if exploited.
Keywords:
Maximal Extractable Value, Web3 Security, Blockchain
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Abstract:
Smart contracts are susceptible to exploitation due to their unique nature. Despite efforts to identify vulnerabilities using fuzzing, symbolic execution, formal verification, and manual auditing, exploitable vulnerabilities still exist and have led to billions of dollars in monetary losses. To address this issue, it is critical that runtime defenses are in place to minimize exploitation risk. In this paper, we present STING, a novel runtime defense mechanism against smart contract exploits. The key idea is to instantly synthesize counterattack smart contracts from attacking transactions and leverage the power of Maximal Extractable Value (MEV) to front run attackers. Our evaluation with 62 real-world recent exploits demonstrates its effectiveness, successfully countering 54 of the exploits (i.e., intercepting all the funds stolen by the attacker). In comparison, a general front-runner defense could only handle 12 exploits. Our results provide a clear proof-of-concept that STING is a viable defense mechanism against smart contract exploits and has the potential to significantly reduce the risk of exploitation in the smart contract ecosystem.
Keywords:
Frontrunning Attack, Web3 Security, Blockchain
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Abstract:
Smart contracts are susceptible to front-running attacks, in which malicious users leverage prior knowledge of upcoming transactions to execute attack transactions in advance and benefit their own portfolios. Existing contract analysis techniques raise a number of false positives and false negatives in that they simplistically treat data races in a contract as front-running vulnerabilities and can only analyze contracts in isolation. In this work, we formalize the definition of exploitable front-running vulnerabilities based on previous empirical studies on historical attacks, and present Nyx, a novel static analyzer to detect them. Nyx features a Datalog-based preprocessing procedure that efficiently and soundly prunes a large part of the search space, followed by a symbolic validation engine that precisely locates vulnerabilities with an SMT solver. We evaluate Nyx using a large dataset that comprises 513 realworld front-running attacks in smart contracts. Compared to six state-of-the-art techniques, Nyx surpasses them by 32.64%-90.19% in terms of recall and 2.89%-70.89% in terms of precision. Nyx has also identified four zero-days in real-world smart contracts.
Keywords:
Behavioral Contract, Smart Contract, Runtime Monitoring
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Abstract:
Ensuring the reliability of smart contracts is of vital importance due to the wide adoption of smart contract programs in decentralized financial applications. However, statically checking many rich properties of smart contract programs can be challenging. On the other hand, dynamic validation approaches have shown promise for widespread adoption in practice. Nevertheless, as part of the programming environment for smart contracts, existing dynamic validation approaches have not provided programmers with a notion to clearly articulate the interface between components, especially for addresses representing opaque contract instances. We argue that the ``design-by-contract'' approach should complement the development of smart contract programs. Unfortunately, there is limited linguistic support for it in existing smart contract languages.
In this paper, we design a Solidity language extension ConSol that supports behavioral contracts. ConSol provides programmers with a modular specification and monitoring system for both functional and latent address behaviors. The key capability of ConSol is to attach specifications to first-class addresses and monitor violations when invoking these addresses. We evaluate ConSol using 20 real-world cases, demonstrating its effectiveness in expressing critical conditions and preventing attacks. Additionally, we assess ConSol's efficiency and compare gas consumption with manually inserted assertions, showing that our approach introduces only marginal gas overhead. By separating specifications and implementations using behavioral contracts, ConSol assists programmers in writing more robust and readable smart contracts.
Keywords:
Deep Learning Security, Large Language Model
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Abstract:
Backdoor attacks have emerged as a prominent threat to natural language processing (NLP) models, where the presence of specific triggers in the input can lead poisoned models to misclassify these inputs to predetermined target classes. Current detection mechanisms are limited by their inability to address more covert backdoor strategies, such as style-based attacks. In this work, we propose an innovative test-time poisoned sample detection framework that hinges on the interpretability of model predictions, grounded in the semantic meaning of inputs. We contend that triggers (e.g., infrequent words) are not supposed to fundamentally alter the underlying semantic meanings of poisoned samples as they want to stay stealthy. Based on this observation, we hypothesize that while the model's predictions for paraphrased clean samples should remain stable, predictions for poisoned samples should revert to their true labels upon the mutations applied to triggers during the paraphrasing process. We employ ChatGPT, a state-of-the-art large language model, as our paraphraser and formulate the trigger-removal task as a prompt engineering problem. We adopt fuzzing, a technique commonly used for unearthing software vulnerabilities, to discover optimal paraphrase prompts that can effectively eliminate triggers while concurrently maintaining input semantics. Experiments on 4 types of backdoor attacks, including the subtle style backdoors, and 4 distinct datasets demonstrate that our approach surpasses baseline methods, including STRIP, RAP, and ONION, in precision and recall.
Keywords:
Jailbreaking, Model Alignment, Large Language Model
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Abstract:
Large Language Models (LLMs) are increasingly employed in numerous applications. It is hence important to ensure that their ethical standard aligns with humans’. However, existing jail-breaking efforts show that such alignment could be compromised by well-crafted prompts. In this paper, we disclose a new threat to LLMs alignment when a malicious actor has access to the top-k token predictions at each output position of the model, such as in all open-source LLMs and many commercial LLMs that provide the needed APIs (e.g., some GPT versions). It does not require crafting any prompt. Instead, it leverages the observation that even when an LLM declines a toxic query, the harmful response is concealed deep within the output logits. We can coerce the model to disclose it by forcefully using low-ranked output tokens during autoregressive output generation, and such forcing is only needed in a very small number of selected output positions. We call it model interrogation. Since our method operates differently from jail-breaking, it has better effectiveness than state-of-theart jail-breaking techniques (92% versus 62%) and is 10 to 20 times faster. The toxic content elicited by our method is also of better quality. More importantly, it is complementary to jail-breaking, and a synergetic integration of the two exhibits superior performance over individual methods. We also find that with interrogation, harmful content can even be extracted from models customized for coding tasks.
