Cryptographic functions have been commonly abused by malware developers to hide malicious behaviors, disguise destructive payloads, and bypass network-based fire- walls. Now-infamous crypto-ransomware even encrypts victim’s computer documents until a ransom is paid. Therefore, de- tecting cryptographic functions in binary code is an appealing approach to complement existing malware defense and forensics. However, pervasive control and data obfuscation schemes make cryptographic function identification a challenging work. Existing detection methods are either brittle to work on obfuscated binaries or ad hoc in that they can only identify specific cryp- tographic functions. In this paper, we propose a novel technique called bit-precise symbolic loop mapping to identify cryptographic functions in obfuscated binary code. Our trace-based approach captures the semantics of possible cryptographic algorithms with bit-precise symbolic execution in a loop. Then we perform guided fuzzing to efficiently match boolean formulas with known reference implementations. We have developed a prototype called CryptoHunt and evaluated it with a set of obfuscated synthetic examples, well-known cryptographic libraries, and malware. Compared with the existing tools, CryptoHunt is a general approach to detecting commonly used cryptographic functions such as TEA, AES, RC4, MD5, and RSA under different control and data obfuscation scheme combinations.
more »
« less
SYMBEXCEL: Automated Analysis and Understanding of Malicious Excel 4.0 Macros
Malicious software (malware) poses a significant threat to the security of our networks and users. In the ever-evolving malware landscape, Excel 4.0 Office macros (XL4) have recently become an important attack vector. These macros are often hidden within apparently legitimate documents and under several layers of obfuscation. As such, they are difficult to analyze using static analysis techniques. Moreover, the analysis in a dynamic analysis environment (a sandbox) is challenging because the macros execute correctly only under specific environmental conditions that are not always easy to create.
This paper presents SYMBEXCEL, a novel solution that leverages symbolic execution to deobfuscate and analyze Excel 4.0 macros automatically. Our approach proceeds in three stages: (1) The malicious document is parsed and loaded in memory; (2) Our symbolic
execution engine executes the XL4 formulas; and (3) Our Engine concretizes any symbolic values encountered during the symbolic exploration, therefore evaluating the execution of each macro under a broad range of (meaningful) environment configurations.
SYMBEXCEL significantly outperforms existing deobfuscation tools, allowing us to reliably extract Indicators of Compromise (IoCs) and other critical forensics information. Our experiments demonstrate the effectiveness of our approach, especially in deobfuscating
novel malicious documents that make heavy use of environment variables and are often not identified by commercial anti-virus software.
more »
« less
- Award ID(s):
- 1704253
- NSF-PAR ID:
- 10346382
- Date Published:
- Journal Name:
- 2022 IEEE Symposium on Security and Privacy (SP)
- Page Range / eLocation ID:
- 1066 to 1081
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The key challenge of software reverse engi- neering is that the source code of the program under in- vestigation is typically not available. Identifying differ- ences between two executable binaries (binary diffing) can reveal valuable information in the absence of source code, such as vulnerability patches, software plagiarism evidence, and malware variant relations. Recently, a new binary diffing method based on symbolic execution and constraint solving has been proposed to look for the code pairs with the same semantics, even though they are ostensibly different in syntactics. Such semantics- based method captures intrinsic differences/similarities of binary code, making it a compelling choice to analyze highly-obfuscated malicious programs. However, due to the nature of symbolic execution, semantics-based bi- nary diffing suffers from significant performance slow- down, hindering it from analyzing large numbers of malware samples. In this paper, we attempt to miti- gate the high overhead of semantics-based binary diff- ing with application to malware lineage inference. We first study the key obstacles that contribute to the performance bottleneck. Then we propose normalized basic block memoization to speed up semantics-based binary diffing. We introduce an unionfind set structure that records semantically equivalent basic blocks. Managing the union-find structure during successive comparisons allows direct reuse of previously computed results. Moreover, we utilize a set of enhanced optimization methods to further cut down the invocation numbers of constraint solver. We have implemented our tech- nique, called MalwareHunt, on top of a trace-oriented binary diffing tool and evaluated it on 15 polymorphic and metamorphic malware families. We perform intra- family comparisons for the purpose of malware lineage inference. Our experimental results show that Malware- Huntcan accelerate symbolic execution from 2.8X to 5.3X (with an average 4.1X), and reduce constraint solver invocation by a factor of 3.0X to 6.0X (with an average 4.5X).more » « less
-
The recent paradigm shift introduced by the Internet of Things (IoT) has brought embedded systems into focus as a target for both security analysts and malicious adversaries. Typified by their lack of standardized hardware, diverse software, and opaque functionality, IoT devices present unique challenges to security analysts due to the tight coupling between their firmware and the hardware for which it was designed. In order to take advantage of modern program analysis techniques, such as fuzzing or symbolic execution, with any kind of scale or depth, analysts must have the ability to execute firmware code in emulated (or virtualized) environments. However, these emulation environments are rarely available and cumbersome to create through manual reverse engineering, greatly limiting the analysis of binary firmware. In this work, we explore the problem of firmware re-hosting, the process by which firmware is migrated from its original hardware environment into a virtualized one. We show that an approach capable of creating virtual, interactive environments in an automated manner is a necessity to enable firmware analysis at scale. We present the first proof-of-concept system aiming to achieve this goal, called PRETENDER, which uses observations of the interactions between the original hardware and the firmware to automatically create models of peripherals, and allows for the execution of the firmware in a fully-emulated environment. Unlike previous approaches, these models are interactive, stateful, and transferable, meaning they are designed to allow the program to receive and process new input, a requirement of many analyses. We demonstrate our approach on multiple hardware platforms and firmware samples, and show that the models are flexible enough to allow for virtualized code execution, the exploration of new code paths, and the identification of security vulnerabilities.more » « less
-
Detecting regression bugs in software evolution, analyzing side-channels in programs and evaluating robustness in deep neural networks (DNNs) can all be seen as instances of differential software analysis, where the goal is to generate diverging executions of program paths. Two executions are said to be diverging if the observable program behavior differs, e.g., in terms of program output, execution time, or (DNN) classification. The key challenge of differential software analysis is to simultaneously reason about multiple program paths, often across program variants. This paper presents HyDiff, the first hybrid approach for differential software analysis. HyDiff integrates and extends two very successful testing techniques: Feedback-directed greybox fuzzing for efficient program testing and shadow symbolic execution for systematic program exploration. HyDiff extends greybox fuzzing with divergence-driven feedback based on novel cost metrics that take into account the control flow graph of the program. Furthermore HyDiff extends shadow symbolic execution by applying four-way forking in a systematic exploration and still having the ability to incorporate concrete inputs in the analysis. HyDiff applies divergence revealing heuristics based on resource consumption and control-flow information to efficiently guide the symbolic exploration, which allows its efficient usage beyond regression testing applications. We introduce differential metrics such as output, decision and cost difference, as well as patch distance, to assist the fuzzing and symbolic execution components in maximizing the execution divergence. We implemented our approach on top of the fuzzer AFL and the symbolic execution framework Symbolic PathFinder. We illustrate HyDiff on regression and side-channel analysis for Java bytecode programs, and further show how to use HyDiff for robustness analysis of neural networks.more » « less
-
The phenomenal growth in use of android devices in the recent years has also been accompanied by the rise of android malware. This reality warrants development of tools and techniques to analyze android apps in large scale for security vetting. Most of the state-of-the-art vetting tools are either based on static analysis or on dynamic analysis. Static analysis has limited success if the malware app utilizes sophisticated evading tricks. Dynamic analysis on the other hand may not find all the code execution paths, which let some malware apps remain undetected. Moreover, the existing static and dynamic analysis vetting techniques require extensive human interaction. To ad- dress the above issues, we design a deep learning based hybrid analysis technique, which combines the complementary strengths of each analysis paradigm to attain better accuracy. Moreover, automated feature engineering capability of the deep learning framework addresses the human interaction issue. In particular, using lightweight static and dynamic analysis procedure, we obtain multiple artifacts, and with these artifacts we train the deep learner to create independent models, and then combine them to build a hybrid classifier to obtain the final vetting decision (malicious apps vs. benign apps). The experiments show that our best deep learning model with hybrid analysis achieves an area under the precision-recall curve (AUC) of 0.9998. In this paper, we also present a comparative study of performance measures of the various variants of the deep learning framework. Additional experiments indicate that our vetting system is fairly robust against imbalanced data and is scalable.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SP46214.2022.9833765
