SAFE: Self-Attentive Function Embeddings for Binary Similarity

The binary similarity problem consists in determining if two functions are similar by only considering their compiled form. Advanced techniques for binary similarity recently gained momentum as they can be applied in several fields, such as copyright disputes, malware analysis, vulnerability detection, etc., and thus have an immediate practical impact. Current solutions compare functions by first transforming their binary code in multi-dimensional vector representations (embeddings), and then comparing vectors through simple and efficient geometric operations. However, embeddings are usually derived from binary code using manual feature extraction, that may fail in considering important function characteristics, or may consider features that are not important for the binary similarity problem. In this paper we propose SAFE, a novel architecture for the embedding of functions based on a self-attentive neural network. SAFE works directly on disassembled binary functions, does not require manual feature extraction, is computationally more efficient than existing solutions (i.e., it does not incur in the computational overhead of building or manipulating control flow graphs), and is more general as it works on stripped binaries and on multiple architectures. We report the results from a quantitative and qualitative analysis that show how SAFE provides a noticeable performance improvement with respect to previous solutions. Furthermore, we show how clusters of our embedding vectors are closely related to the semantic of the implemented algorithms, paving the way for further interesting applications (e.g. semantic-based binary function search).Comment: Published in International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA) 201

VulMatch: Binary-level Vulnerability Detection Through Signature

Similar vulnerability repeats in real-world software products because of code reuse, especially in wildly reused third-party code and libraries. Detecting repeating vulnerabilities like 1-day and N-day vulnerabilities is an important cyber security task. Unfortunately, the state-of-the-art methods suffer from poor performance because they detect patch existence instead of vulnerability existence and infer the vulnerability signature directly from binary code. In this paper, we propose VulMatch to extract precise vulnerability-related binary instructions to generate the vulnerability-related signature. VulMatch detects vulnerability existence based on binary signatures. Unlike previous approaches, VulMatch accurately locates vulnerability-related instructions by utilizing source and binary codes. Our experiments were conducted using over 1000 vulnerable instances across seven open-source projects. VulMatch significantly outperformed the baseline tools Asm2vec and Palmtree. Besides the performance advantages over the baseline tools, VulMatch offers a better feature by providing explainable reasons during vulnerability detection. Our empirical studies demonstrate that VulMatch detects fine-grained vulnerability that the state-of-the-art tools struggle with. Our experiment on commercial firmware demonstrates VulMatch is able to find vulnerabilities in real-world scenario.Comment: 15 pages IEEE journal templat

Function Representations for Binary Similarity

The binary similarity problem consists in determining if two functions are similar considering only their compiled form. Advanced techniques for binary similarity recently gained momentum as they can be applied in several fields, such as copyright disputes, malware analysis, vulnerability detection, etc. In this paper we describe SAFE, a novel architecture for function representation based on a self-attentive neural network. SAFE works directly on disassembled binary functions, does not require manual feature extraction, is computationally more efficient than existing solutions, and is more general as it works on stripped binaries and on multiple architectures. Results from our experimental evaluation show how SAFE provides a performance improvement with respect to previoussolutions. Furthermore, we show how SAFE can be used in widely different use cases, thus providing a general solution for several application scenarios

Revisiting Binary Code Similarity Analysis using Interpretable Feature Engineering and Lessons Learned

Binary code similarity analysis (BCSA) is widely used for diverse security applications such as plagiarism detection, software license violation detection, and vulnerability discovery. Despite the surging research interest in BCSA, it is significantly challenging to perform new research in this field for several reasons. First, most existing approaches focus only on the end results, namely, increasing the success rate of BCSA, by adopting uninterpretable machine learning. Moreover, they utilize their own benchmark sharing neither the source code nor the entire dataset. Finally, researchers often use different terminologies or even use the same technique without citing the previous literature properly, which makes it difficult to reproduce or extend previous work. To address these problems, we take a step back from the mainstream and contemplate fundamental research questions for BCSA. Why does a certain technique or a feature show better results than the others? Specifically, we conduct the first systematic study on the basic features used in BCSA by leveraging interpretable feature engineering on a large-scale benchmark. Our study reveals various useful insights on BCSA. For example, we show that a simple interpretable model with a few basic features can achieve a comparable result to that of recent deep learning-based approaches. Furthermore, we show that the way we compile binaries or the correctness of underlying binary analysis tools can significantly affect the performance of BCSA. Lastly, we make all our source code and benchmark public and suggest future directions in this field to help further research.Comment: 22 pages, under revision to Transactions on Software Engineering (July 2021

THE SCALABLE AND ACCOUNTABLE BINARY CODE SEARCH AND ITS APPLICATIONS

The past decade has been witnessing an explosion of various applications and devices. This big-data era challenges the existing security technologies: new analysis techniques should be scalable to handle “big data” scale codebase; They should be become smart and proactive by using the data to understand what the vulnerable points are and where they locate; effective protection will be provided for dissemination and analysis of the data involving sensitive information on an unprecedented scale. In this dissertation, I argue that the code search techniques can boost existing security analysis techniques (vulnerability identification and memory analysis) in terms of scalability and accuracy. In order to demonstrate its benefits, I address two issues of code search by using the code analysis: scalability and accountability. I further demonstrate the benefit of code search by applying it for the scalable vulnerability identification [57] and the cross-version memory analysis problems [55, 56]. Firstly, I address the scalability problem of code search by learning “higher-level” semantic features from code [57]. Instead of conducting fine-grained testing on a single device or program, it becomes much more crucial to achieve the quick vulnerability scanning in devices or programs at a “big data” scale. However, discovering vulnerabilities in “big code” is like finding a needle in the haystack, even when dealing with known vulnerabilities. This new challenge demands a scalable code search approach. To this end, I leverage successful techniques from the image search in computer vision community and propose a novel code encoding method for scalable vulnerability search in binary code. The evaluation results show that this approach can achieve comparable or even better accuracy and efficiency than the baseline techniques. Secondly, I tackle the accountability issues left in the vulnerability searching problem by designing vulnerability-oriented raw features [58]. The similar code does not always represent the similar vulnerability, so it requires that the feature engineering for the code search should focus on semantic level features rather than syntactic ones. I propose to extract conditional formulas as higher-level semantic features from the raw binary code to conduct the code search. A conditional formula explicitly captures two cardinal factors of a vulnerability: 1) erroneous data dependencies and 2) missing or invalid condition checks. As a result, the binary code search on conditional formulas produces significantly higher accuracy and provides meaningful evidence for human analysts to further examine the search results. The evaluation results show that this approach can further improve the search accuracy of existing bug search techniques with very reasonable performance overhead. Finally, I demonstrate the potential of the code search technique in the memory analysis field, and apply it to address their across-version issue in the memory forensic problem [55, 56]. The memory analysis techniques for COTS software usually rely on the so-called “data structure profiles” for their binaries. Construction of such profiles requires the expert knowledge about the internal working of a specified software version. However, it is still a cumbersome manual effort most of time. I propose to leverage the code search technique to enable a notion named “cross-version memory analysis”, which can update a profile for new versions of a software by transferring the knowledge from the model that has already been trained on its old version. The evaluation results show that the code search based approach advances the existing memory analysis methods by reducing the manual efforts while maintaining the reasonable accuracy. With the help of collaborators, I further developed two plugins to the Volatility memory forensic framework [2], and show that each of the two plugins can construct a localized profile to perform specified memory forensic tasks on the same memory dump, without the need of manual effort in creating the corresponding profile