Tab Tianyi Zhang, Bjorn De Sutter, Christian Collberg, Bart Coppens, Waleed Mebane, Empirical Reverse Engineering and Software Protection Studies with Students, 2026.
Tab Tianyi Zhang, Bjorn De Sutter, Christian Collberg, Bart Coppens, Waleed Mebane, Empirical Reverse Engineering and Software Protection Studies with Students, 2026.
Christian Collberg, ISSUES IN EVALUATION OF REVERSE ENGINEERING AND SOFTWARE PROTECTION, Keynote address, ARES-25, 2025.
Tab (Tianyi) Zhang, Claire Taylor, Bart Coppens, Waleed Mebane, Christian Collberg, Bjorn De Sutter, reAnalyst: Scalable annotation of reverse engineering activities, 2025.
Benjamin Mariano, Ziteng Wang, Shankara Pailoor, Christian Collberg, Işil Dillig, Control-Flow Deobfuscation using Trace-Informed Compositional Program Synthesis, 2024.
“Additionally, we run a detailed performance evaluation of Chisel on 100 programs generated by Tigress [14] and find that our technique can scale to quite complex programs, including obfuscated programs with nearly 150,000 lines of code.”
Thomas Faingnaert, Tab Zhang, Willem Van Iseghem, Gertjan Everaert, Bart Coppens, Christian Collberg, Bjorn De Sutter, Tools and Models for Software Reverse Engineering Research, 2024.
Claire Taylor and Christian Collberg, Getting RevEngE: A System for Analyzing Reverse Engineering Behavior [MALCON 2019 Outstanding Paper Award], 2019.
Christian Collberg, Tigress: A Source-to-Source-ish Obfuscation Tool (Keynote address, SSPREW-8, 2018), 2018.
Christian Collberg, Code Obfuscation: Why is This Still a Thing? [Keynote Address, CODASPY 2018], 2018.
Jon Stephens, Babak Yadegari, Christian Collberg, Saumya Debray, and Carlos Scheidegger, Probabilistic Obfuscation through Covert Channels [3rd IEEE European Symposium on Security and Privacy, 2018], 2018.
Sebastian Banescu, Christian Collberg, Alexander Pretschner, Predicting the Resilience of Obfuscated Code Against Symbolic Execution Attacks via Machine Learning [USENIX Security'17], 2017.
“We have used five obfuscating transformations offered by Tigress [13], in order to generate five obfuscated versions of each of the 4608 programs generated by our code generator and the 11 non-cryptographic hash functions.”
Sebastian Banescu, Christian Collberg, Vijay Ganesh, Zack Newsham, Alexander Pretschner, Obfuscation Against Symbolic Execution Attacks, [Outstanding Paper Award, ACSAC'16], 2016.
“Therefore, in this work we use the Tigress C Obfuscator [18] and Obfuscator-LLVM [37], which operate at the C source code level, respectively LLVM intermediate representation level.”
Christian Collberg, Engineering Code Obfuscation [Eurocrypt 2016 invited talk], 2016.
Yuichiro Kanzaki, Akito Monden, Christian Collberg, Code Artificiality: A Metric for the Code Stealth Based on an N-gram Model [2015 IEEE/ACM 1st International Workshop on Software Protection], 2015.
“The obfuscated programs were transformed manually except for Cenca and Cencd , which were automatically transformed by the Tigress obfuscation tool [11], [14].”
Yuichiro Kanzaki, Clark Thomborson, Akito Monden, Christian S Collberg, Pinpointing and hiding surprising fragments in an obfuscated program, 5th Program Protection and Reverse Engineering Workshop [PPREW 2015], 2015.
“To avoid introducing bias from a manual obfuscation, and to have a reproducible experimental result, we used Version 2.0 of the Tigress obfuscation tool [6,7].”
Christian Collberg, Sam Martin, Jonathan Myers, Jasvir Nagra, Distributed Application Tamper Detection via Continuous Software Updates [ACSAC '12], 2012.
Sangjun An, Seoksu Lee, Eun-Sun Cho, Static Detection of Core Structures in Tigress Virtualization-Based Obfuscation Using an LLVM Pass, 2026.
“The experimental dataset was generated by applying VM-based obfuscation to C-based source code using the Tigress 4.0.11 obfuscation tool.”
Byunggeon Choi; Hongjoo Jin; Dong Hoon Lee; Wonsuk Choi, Toward Efficient Deobfuscation via Large Language Models, 2026.
“This study considers seven obfuscation methods, including Mixed Boolean-Arithmetic, Control Flow Flattening, and Opaque Predicate, each applied using Tigress and Obfuscator LLVM.”
Ashwin Sudhir, Zion Leonahenahe Basque, Wil Gibbs, Ati Priya Bajaj, Pulkit Singh Singaria, Mitchell Zakocs, Jie Hu, Moritz Schloegel, Tiffany Bao, Adam Doupe, Yan Shoshitaishvili, Ruoyu Wang, Pushan: Trace-Free Deobfuscation of Virtualization-Obfuscated Binaries, 2026.
“For Tigress, we used the random function generator to produce 1,000 hash functions, which we then obfuscated using virtualization along with the following configurations:”
Jonathan Gobbo, Federica Sarro and Paolo Falcarin, The Ghidra Metrics Toolkit for obfuscated native code, 2026.
“We developed GhidraMetricsToolkit, an open-source Ghidra plug-in to compute a collection of complexity metrics on native code. We tested the plug-in on a set of 61 obfuscated binaries generated from two programs using a set of 9 transformations provided by Tigress, each using four different configurations.”
Soyeon Baek; Sungho Lee, Deobfuscation of Control Flow Flattening Based on Abstract Interpretation, 2026.
“We chose OLLVM and Tigress to CFF-obfuscate the benchmark; we used the "control flow flattening" option of OLLVM and the "flattening" option of Tigress in the CFF-obfuscation.”
Seyedreza Mohseni, Sarvesh Baskar, Edward Raff, Manas Gaur, Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks, 2026.
“We systematically analyze the performance of selected LLMs using CoT prompting on obfuscated code samples generated by O-LLVM and Tigress.”
Caroline König, David Schmidt, Philip König, Patrick Felbauer, Sebastian Schrittwieser, SaMBA: Increasing Mixed Boolean-Arithmetic Complexity Through Equality Saturation, 2026.
“To enable the use of SaMBA for real-world code, we integrated with the state-of-the-art C source-to-source obfuscator Tigress [2] through its plugin infrastructure.”
Yitong Wang, Pengfei Yu, Hao Han, Jingjing Gu, Fengyuan Xu, Xinyi Huang, WAMO: Toward Secure Browser Inference via Web Model Obfuscation in WebAssembly, 2026.
“The compilation path of our Wasm model files goes from C++ through IR to WebAssembly, so we evaluated three mainstream compilers at successive stages, including Tigress [1] (C++ level), Wasm-mutate [7] (IR level), and WASMixer [8] (Wasm level).”
Jaehyung Lee, Seoksu Lee, Eunsun Cho, Woosuk Lee, Simplifying Mixed Boolean-Arithmetic Obfuscation by Program Synthesis and Equality Saturation, 2026.
“The 500 MBA expressions are obfuscated with the EncodeArithmetic scheme [18] in the Tigress obfuscator [4].”
Sangjun An, Hyeyeon Park, Yejin Son, Seoksu Lee, Eun-Sun Cho, Towards LLM-Based Analysis of Virtualization-Obfuscated Code through Automated Data Generation, 2026.
“It first verifies whether automated structural analysis can correctly identify virtualization components generated by Tigress, then uses the resulting labels to fine-tune a BERT model and reconstruct CFG visualizations from the inferred results.”
Tetsuya KITAOKA, Yuichiro KANZAKI, Takashi ISHIO, Kazumasa SHIMARI, Kenichi MATSUMOTO, Evaluating the Resilience of Obfuscated Code Against LLM-based Function Name Recovery in MATE Attacks, 2026.
“Since we employ Tigress as an obfuscation tool that meets the requirements for the traceability of function names described in Sect.”
Ran Wei , Hui Shu , Fei Kang , Xiaobing Xiong , Yang Lan , Cong Li, FunSp: countering binary code similarity detection through function splitting, 2026.
“Given that our work aims to improve obfuscation effectiveness while maintaining low runtime overhead, we select O-LLVM and Tigress as the primary baselines in our comparative experiments.”
Xianyu Zou, Xiaoli Gong, Jin Zhang, Shiyang Li, Pen-Chung Yew, XuanJia: A Comprehensive Virtualization-Based Code Obfuscator for Binary Protection, 2026.
“Compared to Tigress, which incurs a runtime overhead ranging from 42×to 247×, XuanJia exhibits higher overhead.”
Logan Mebane; Ajay Kumara Makanahalli Annaiah, Exploring the Capabilities of LLMs in Binary Decompilation and Deobfuscation, 2026.
“The left one cuts off because it just repeats the same generic incrementation of int* variables up to the 178th declaration. the simple calculator using virtualization C obfuscation with Tigress [19], which transforms code into a custom language interpreted at runtime [20].”
Li Hu, Xiuwei Shang, Jieke Shi, Shaoyin Cheng, Junqi Zhang, Gangyang Li, Zhou Yang, Weiming Zhang, David Lo, Can LLMs Deobfuscate Binary Code? A Systematic Analysis of Large Language Models into Pseudocode Deobfuscation, 2026.
“We collect four commonly used obfuscators: OLLVM [62], Hikari [63], Tigress [64], and Alcatraz [65], representing both open-source and commercial obfuscation tools.”
Yiming Fan, Jun Yeon Won, Ding Zhu, Melih Sirlanci, Mahdi Khalili, Carter Yagemann, EXHIB: A Benchmark for Realistic and Diverse Evaluation of Function Similarity in the Wild, 2026.
“We used three obfuscators: Obfuscator-LLVM [26], Hikari [58], and Tigress [5].”
Markus Puura, Sid Touati, Selecting Graph Representations for Cross-Language Program Recognition, 2026.
“We have developed a tool for generating variants of C programs by using both the optimization passes of LLVM's opt optimizer and the Tigress [33] code obfuscator.”
Yuchen Chen, Yuan Xiao, Chunrong Fang, Zhenyu Chen, Baowen Xu, DuCodeMark: Dual-Purpose Code Dataset Watermarking via Style-Aware Watermark-Poison Design, 2026.
“For code obfuscation attacks, we employ two widely adopted obfuscation tools, Tigress and ProGuard, to obfuscate 100 DuCodeMark-protected C and Java samples, respectively.”
Nicolò Altamura, Enrico Bragastini, Marco Campion, Mila Dalla Preda, Assessing the Effectiveness of the Tigress Obfuscator Against MOPSA and BinaryNinja, 2025.
An empirical evaluation of how far Tigress degrades the precision of two static analysers, the abstract interpreter MOPSA and BinaryNinja. The authors drive Tigress directly, choosing among its five Flatten dispatchers (only switch suited their setup) and selecting which AddOpaque dead-branch kinds to use.
“We use the Tigress obfuscation tool, a well-documented academic tool for source-tosource obfuscation of C programs, to apply a set of lightweight obfuscation techniques such as data encoding, control flow flattening, opaque predicates and we extended Tigress to include obfuscations based on opaque constants [27].”
John Rubin, Svante Hannevind, Measuring the performance overhead of obfuscating code transformations, 2025.
“The obfuscating transformation techniques used in this study are from the Tigress obfuscation framework [13].”
Roxane Cohen, Robin David, Riccardo Mori, Florian Yger, Fabrice Rossi, Experimental Study of Binary Diffing Resilience on Obfuscated Programs, 2025.
“For each Tigress and OLLVM-14 obfuscations, QBinDiff is applied with these three configurations on the zlib project, compiled with -O0.”
Naiqian Zhang; Dongpeng Xu; Jiang Ming; Jun Xu; Qiaoyan Yu, Inspecting Virtual Machine Diversification Inside Virtualization Obfuscation, 2025.
“The commercial versions of these tools (Code Virtualizer 3.1.4, VMProtect 3.6.0, Tigress 3.3.2) are used in this work.”
Hyeongchang Jeon; Seoksu Lee; Eun-Sun Cho, An Enhanced Opaque Predicate Detection Method with Synthesis and Recursive Matching, 2025.
“OLLVM and Tigress were tested with Linux ELF, while Code Virtualizer was applied to Windows PE files.”
Byunggeon Choi, Hongjoo Jin, Dong Hoon Lee, Wonsuk Choi, ChatDEOB: An Effective Deobfuscation Method Based on Large Language Model, 2025.
“Specifically, we generate the obfuscated source code using the Tigress obfuscation tool and LLVM.”
XUEZIXIANG LI, SHENG YU, HENG YIN, ALMOND: Learning an Assembly Language Model for 0-Shot Code Obfuscation Detection, 2025.
“For this evaluation, we selected the AddOpaque, Split, Merge, and Flatten obfuscation techniques from Tigress to obfuscate the source code and then compiled it into binary form.”
Yan Cao, Zhizhuang Zhou, Yan Zhuang, Advancing Code Obfuscation: Novel Opaque Predicate Techniques to Counter Dynamic Symbolic Execution, 2025.
David Beste, Grégoire Menguy, Hossein Hajipour, Mario Fritz, Antonio Emanuele Cinà, Sébastien Bardin, Thorsten Holz, Thorsten Eisenhofer, Lea Schönherr, Exploring the Potential of LLMs for Code Deobfuscation, 2025.
“To increase diversity, we vary the parameters for the chosen transformations using the recommendations from the Tigress documentation.”
Tanapoom Sermchaiwong, Jiasi Shen, Dynamic Graph-based Fingerprinting of In-browser Cryptomining, 2025.
“We apply the following tigress obfuscations to three of the miners which successfully ran with Tigress and Wasabi instrumentation applied.”
Siliang Qin, Fengrui Yang, Hao Wang, Bolun Zhang, Zeyu Gao, Chao Zhang, Kai Chen, Tady: A Neural Disassembler without Structural Constraint Violations, 2025.
“To assess robustness against obfuscation, we use OLLVM14.0 [15] (LLVM IR level), Tigress [1] (source code level), and binobf [24] (binary-level anti-disassembly).”
Xiao Li, Jiajia Sun, Yongguang Zhang, Xiaokang Yin, Shengli Liu, Binary Similarity Detection Based on Intermediate Representation Graph Fusion, 2025.
“In this study, we produce equivalent obfuscated code using both O-LLVM and Tigress by compiling a complete dataset comprising all acknowledged obfuscation techniques.”
Daniele Canavese, Leonardo Regano, Bjorn De Sutter, and Cataldo Basile, Automatic Selection of Protections to Mitigate Risks Against Software Applications, 2025.
“At the time of writing, the ESP supports Tigress, a source code obfuscator developed at the University of Arizona, and the ASPIRE Compiler Tool Chain (ACTC), which automates the deployment of SP techniques developed in the ASPIRE FP-7 project.”
Lambang Akbar Wijayadi, Yuancheng Jiang, Roland H.C. Yap, Zhenkai Liang, Zhuohao Liu, Evaluating Disassembly Errors With Only Binaries, 2025.
“We evaluate binaries from the following software: RAR 7.0 [23] (a popular archiver) with rar and unrar binaries, Tigress 3.1 [24] with the cilly.native binary, CUDA Binary Utilities [35] 12.4 with the nvdisasm binary, and PNGOUT 20200115 [36] with the pngout binary.”
Authors: Moses Ike, Keaton Sadoski, Romuald Valme, Burak Sahin, Saman Zonouz, Wenke Lee, Your Control Host Intrusion Left Some Physical Breadcrumbs: Physical Evidence-Guided Post-Mortem Triage of SCADA Attacks, 2025.
Uses the Tigress RandomFuns transformation, with randomly varied options for control flow, basic blocks, loops, operators, struct size and bounds, to synthesize the varied program corpus the investigation is evaluated on.
Jiyong Uhm, Yujeong Kwon, Hyungjoon Koo, On the Learnability, Robustness, and Adaptability of Deep Learning Models for Obfuscation-applied Code, 2025.
“With two publicly available obfuscation tools, Tigress [10] that operates at the source level and Obfuscator-LLVM (O-LLVM) [32] that works at at the IR level, we generate obfuscated executables.”
Roxane Cohen, Robin David, Florian Yger, Fabrice Rossi, Identifying Obfuscated Code Through Graph-Based Semantic Analysis of Binary Code, 2025.
“We investigate more advanced obfuscation techniques than those provided by OLLVM [14] by incorporating Tigress [3] in our experiments.”
Francesco Sattolo, Performance estimation of obfuscated applications with software metrics, 2024.
“For this reason, we decided to rely on one of the few publicly available obfuscators, Tigress [6].”
Hongjoo Jin , Jiwon Lee, Sumin Yang, Kijoong Kim and Dong Hoon Lee, A Framework to Quantify the Quality of Source Code Obfuscation, 2024.
“To evaluate the proposed framework, we used both commercial and open-source obfuscation tools, including Stunnix C/C++ Obfuscator [6], Semantic Designs C-GCC4 Obfuscator [7], Tigress Obfuscator [8], and Obfuscator LLVM [9].”
Håkon Harnes , Donn Morrison, Cryptic Bytes: WebAssembly Obfuscation for Evading Cryptojacking Detection, 2024.
“We obfuscate a diverse set of applications, including utilities, games, and crypto miners, using state-of-the-art obfuscation tools like Tigress and wasm-mutate, as well as our novel tool, emcc-obf.”
Naiqian Zhang, Zheyun Feng, Dongpeng Xu, An In-Depth Analysis of the Code-Reuse Gadgets Introduced by Software Obfuscation, 2024.
“In our study, we obfuscate 900 programs from an obfuscation benchmark [3] with four well-known obfuscators in academia and industry, namely Tigress [12], Obfuscator LLVM [20], VMProtect [33] and Code Virtualizer [24].”
Seoksu Lee, Hyeongchang Jeon, Eun-Sun Cho, Simplifying MBA Expression Using E-Graphs, 2024.
“We can also see that the expressions used in the Tigress and Qsynth customEA datasets are simplified to 70%, which is about 30% less than original MBA expressions.”
HY Kim, DH Lee, CatchFuzz: Reliable active anti-fuzzing techniques against coverage-guided fuzzer, 2024.
“We extracted the Coverage-Faker and applied virtualization obfuscation using Tigress (Collberg, 2015), a renowned commercial obfuscation tool.”
Mila Dalla Preda, Michele Ianni, Exploiting number theory for dynamic software watermarking, 2024.
“We tested the robustness to obfuscation too, by compiling the programs after obfuscating them by means of the Tigress C Obfuscator.”
José Miguel Aragón-Jurado; Javier Jareóo; Juan Carlos De la Torre; Patricia Ruiz; Bernabé Dorronsoro, Two-level Software Obfuscation with Cooperative Co-evolutionary Algorithms, 2024.
“E XPERIMENTAL S ETTING This study makes use of Tigress obfuscator version 3.1 to apply the obfuscation transformations at the source code level.”
Sebastian Raubitzek, Sebastian Schrittwieser, Elisabeth Wimmer, Kevin Mallinger, Obfuscation undercover: Unraveling the impact of obfuscation layering on structural code patterns, 2024.
“Starting from a set of 85 C programs, we created a comprehensive research dataset based on the Tigress obfuscator introduced by [5].”
Héctor D. Menéndez, Guillermo Suárez-Tangil, Summary of ObfSec: Measuring the Security of Obfuscations from a Testing Perspective, 2024.
“To conduct our experiments, we considered four state-of-the-art obfuscation tools for C commonly employed in related research [2]: Tigress, LLVM-O, the commercial Stunnix (CXX), and the system tool CObfusc.”
Sebastian Raubitzek, Sebastian Schrittwieser, Caroline Lawitschka, Kevin Mallinger, Andreas Ekelhart and Edgar Weippl, Code Obfuscation Classification Using Singular Value Decomposition on Grayscale Image Representations, 2024.
“For the obfuscated binaries, we used the state-of-the-art source-to-source obfuscator Tigress.”
Z Tian, R He, H Zhao, L Chen, Function-Level Code Obfuscation Detection Through Self-Attention-Guided Multi-Representation Fusion, 2024.
J Royer, F Tronel, Y Vinçont, V Eléonore, Automatic deobfuscation of virtualized binaries with LLVM, 2024.
“We use this tool to partially deobfuscate binaries obfuscated with the Tigress obfuscator.”
P Vivekanandan, BinSimStack: Learning from Multifaceted Program Representation Using Graph-Synchronized Language Model, 2024.
“We also evaluate the robustness of the framework by cascading Tigress EncodeArithmetic (Mixed-Boolean Arithmetic) with Flatten.”
Zhoukai Wang, Zuoyan Xu, Yaling Zhang, Xin Song, Yichuan Wang, Research on Code Virtualization Methods for Cloud Applications, 2024.
“We virtualized five C examples using Tigress, VMProtect, and VMENP, and attempted to de-virtualize them.”
Adrian Van den Broeck, Deobfuscatie van Packers en Self-Modifying Code en Visualisatie van JIT-Gegenereerde Code, 2024.
Dutch. Uses Tigress as the obfuscator throughout -- "the obfuscation tool we have used" -- to generate the self-modifying and packed binaries the thesis then deobfuscates and visualizes.
“De vorm van self-modifying code obfuscatie die wordt toegepast door Tigress, de obfuscatietool die we hebben gebruikt en later zullen beschrijven, vereist ook de aanwezigheid van opaque predicates en een grote hoeveelheid entropie.”
Tetsuya Kitaoka; Yuichiro Kanzaki; Takashi Ishio; Kazumasa Shimari; Kenichi Matsumoto, Initial Investigation of Behavioral Changes of Obfuscated Programs Caused by Code Optimization, 2024.
“We conducted an experiment with 28 obfuscation methods that have been evaluated by Banescu et al. [4], which is based on five obfuscation methods implemented in Tigress 3.1: Encode Arithmetic (EncA), Encode Literals (EncL), Flatten (Flat), Add Opaque (AddO4 or AddO16, depending on the number of opaque predicates inserted), and Virtualize (Virt).”
Kim Tae-hyun, Ahn Yu-jin, Pyeon Go-un, Lee Seong-ho, Experimental and Analysis of Code Clone Detection Performance of Large Language Models for Obfuscated Code, 2024.
Davide Scarr`a, Exploring the Inner Workings of (anti)cheat Tools, 2024.
“For this last example we use Tigress to virtualize our simple program, understand the process of virtualization and compare the original code with the virtualized code.”
Aragón Jurado, José MiguelAuthority; Jareño Dorado, JavierAuthority; Torre Macías, Juan Carlos de laAuthority; Ruiz Villalobos, PatriciaAuthority; Dorronsoro Díaz, Bernabé, Ofuscación de Software en dos Niveles usando Algoritmos Cooperativos Coevolutivos, 2024.
Spanish. Searches for the best sequence of Tigress transformations combined with LLVM optimization passes, using a cooperative co-evolutionary genetic algorithm with one species per framework.
“En este trabajo, definimos un nuevo problema de optimización combinatoria para un método de ofuscación en dos niveles que utiliza transformaciones de ofuscación típicas, las proporcionadas por el framework Tigress, así como las optimizaciones clásicas, las del framework de compilación LLVM.”
Xuangan Xiao, Yizhuo Wang, Yikun Hu∗, and Dawu Gu, XVMP: An LLVM-based Code Virtualization Obfuscator, 2023.
“Strength of X VMP Obfuscation In this experiment, we use an automated de-virtualization tool to analyze the obfuscated programs of X VMP and Tigress to evaluate the strength and effectiveness of X VMP.”
A Shroyer, DM Swany, Detecting Standard Library Functions in Obfuscated Code, 2023.
“For this work, we use Tigress to augment both the size and diversity of our dataset.”
Sebastian Schrittwieser, Elisabeth Wimmer, Kevin Mallinger, Patrick Kochberger, Caroline Lawitschka, Sebastian Raubitzek, Edgar R. Weippl, Modeling Obfuscation Stealth Through Code Complexity, 2023.
“Tigress was developed based on CIL [29] and MyJit3 and is able to protect C source code with a variety of obfuscation methods.”
T Kitaoka, A Framework to Evaluate the Reliability of Obfuscating Transformations in Program Code, 2023.
“The first experiment uses a collection of programs in ObfusEval to evaluate the reliability of existing 43 obfuscating transformations implemented in well-known obfuscation tools, Tigress [5], [6] and Obfuscator-LLVM [7], [8].”
Abdullah Qasem, Mourad Debbabi, Bernard Lebel, Marthe Kassouf, Binary Function Clone Search in the Presence of Code Obfuscation and Optimization over Multi-CPU Architectures, 2023.
“We use (M1) model detailed earlier to generate the embeddings for all obfuscated binary functions by tigress in Dataset-IV.”
C Lawitschka, P König, ER Weippl, Obfuscation-Resilient Semantic Functionality Identification Through Program Simulation, 2023.
“We have chosen the musl-gcc wrapper for comparability reasons because it is also used by the Tigress obfuscator.”
Friptu, Petru-Ştefan, Learning white-box: Applying machine learning to identify white-box related functions in binaries, 2023.
“For run 2 (DES obfuscated with Tigress), the functions that are marked as possible white-box related are the key scheduling function, part of the encryption function which iterates over the DES rounds and the initialization of the substitution box.”
Geri Revay, DON’T FLATTEN YOURSELF: RESTORING MALWARE WITH CONTROL-FLOW FLATTENING OBFUSCATION, 2023.
“In this paper, we described the concept of the control-flow flattening obfuscation technique and demonstrated its effects on a simple piece of malware using the Tigress framework.”
Pierre Graux, Jean-François Lalande, Valérie Viet Triem Tong, Pierre Wilke, OATs’inside: Retrieving Object Behaviors From Native-based Obfuscated Android Applications,, 2023.
“We also evaluate the robustness of OATs'inside using obfuscated unit tests using the Tigress obfuscator.”
Bin Li; Yufeng Tang; Yupeng Zhang; Hengxing Liu; Di Li; Zheng Gong, Cryptanalysis of Space-Hard Ciphers under Strong White-Box Security, 2023.
“Next, we introduce EncodeData transformation of Tigress obfuscator for obfuscating LUTs and point out the weakness of EncodeData transformation.”
Bin Li; Yufeng Tang; Yupeng Zhang; Hengxing Liu; Di Li; Zheng Gong, DEEP LEARNING FOR OBFUSCATED CODE ANALYSIS, Alexander Shroyer, 2023.
“This work uses Tigress to augment a selection of 47 C programs and ultimately obtains a distribution of 9400 programs representing the 47 original categories.”
Naiqian Zhang; Daroc Alden; Dongpeng Xu; Shuai Wang; Trent Jaeger; Wheeler Ruml, No Free Lunch: On the Increased Code Reuse Attack Surface of Obfuscated Programs, 2023.
“We use Tigress [2] and Obfuscator-LLVM [1], two popular obfuscators for software protection in academia and industry.”
Pietro Borrello, Taming Complex Bugs in Secure Systems, 2023.
“We leverage the Tigress framework [76] to generate functions appropriate as reverse engineering targets with a desired complexity and structure.”
Huaijin Wang, Pingchuan Ma, Shuai Wang, Qiyi Tang, Sen Nie, Shi Wu, sem2vec: Semantics-aware Assembly Tracelet Embedding,, 2023.
“To this end, we employ another commonly used obfuscator, Tigress [17].”
Jianyi Zhang , Zhenkui Li , Yudong Liu , Zezheng Sun ,Zhiqiang Wang, SAFTE: A self-injection based anti-fuzzing technique, 2023.
“These experiments included testing the original program, a program protected with compression and obfuscation techniques (UPX), a program protected with virtualization techniques (Tigress), and a program protected using SAFTE.”
Cataldo Basile, Bjorn De Sutter, Daniele Canavese a, Leonardo Regano, Bart Coppens, Design, implementation, and automation of a risk management approach for man-at-the-End software protection, 2023.
“We also added four instances of obfuscation using Tigress, i.e., the ones marked in Table 5.”
Visentin, Thomas, Obfuscation complexity optimization, 2022.
F Lopes, Resilience of code obfuscation to optimization and fuzzing, 2022.
I Di Pietro, Overhead prediction in obfuscated programs, 2022.
“Subsequently, each application was obfuscated using Tigress , a tool developed by the University of Arizona.”
Michael Pucher, Christian Kudera, Georg Merzdovnik, Detecting Obfuscated Function Clones in Binaries using Machine Learning, 2022.
“To this end, OFCI aims to perform function clone search on functions virtualized with Tigress [7], which works by virtualizing functions separately.”
Shrenik Bhansali, Ahmet Aris, Abbas Acar, Harun Oz, and A. Selcuk Uluagac, A First Look at Code Obfuscation for WebAssembly, 2022.
“For this aim, we used Tigress, a free to use C obfuscator that has been widely used in the literature [4].”
Deepak Adhikari, J. Todd McDonald, Todd R. Andel, Joseph D. Richardson, Argon: A Toolbase for Evaluating Software Protection Techniques Against Symbolic Execution Attacks, 2022.
“For purposes of Argon, we chose to integrate a standard and well-known open source obfuscator known as Tigress [6].”
Seoyeon Kang, Jeongwoo Kim, Eun-Sun Cho, Seokwoo Choi, Program Synthesis-based Simplification of MBA Obfuscated Malware with Restart Strategies, 2022.
A. Shroyer and D. M. Swany, Data Augmentation for Code Analysis, 2022.
“This work uses Tigress to augment a selection of 47 C programs and ultimately obtains a distribution of 9400 programs representing the 47 original categories.”
K Tang, Z Shan, C Zhang, L Xu, M Qiao, F Liu, DFSGraph: Data Flow Semantic Model for Intermediate Representation Programs Based on Graph Network, 2022.
“In Tigress C obfuscator, there are too many obfuscation options, we select five representative options: addOpaque, EncodeLiterals, Virtualize, Flatten, EncodeArithmetic.”
Claire Taylor, Remotely Observing Reverse Engineers to Evaluate Software Protection, 2022.
“An example set of tigress arguments which obfuscate a given file with encoded arithmetic, virtualization, and self-modifying code.”
Micha Horlboge, Erwin Quiring, Roland Meyer, Konrad Rieck, I still know it’s you! On Challenges in Anonymizing Source Code, 2022.
“For our experiments, we make use of two common obfuscators, Stunnix [53] and Tigress [16].”
Farzaneh Abazari, Enrico Branca, Evgeniya Novikova, Natalia Stakhanova, Language and Platform Independent Attribution of Heterogeneous Code, 2022.
“In our experiments, we use Tigress [40], an obfuscator tool designed for the C language.”
Zhenzhou Tian, Hengchao Mao, Yaqian Huang, Jie Tian, Jinrui Li, Fine-Grained Obfuscation Scheme Recognition on Binary Code, 2022.
“The specific obfuscation algorithms applied include the -bcf, -fla and -sub options in OLLVM, as well as the -AddOpaque, -Virtualize and -EncodeLiterals options in Tigress.”
Seong-Kyun Mok, Seoyeon Kang, Jeongwoo Kim, Eun-Sun Cho, Seokwoo Choi, SSLEM: A Simplifier for MBA Expressions based on Semi-linear MBA Expressions and Program Synthesis, 2022.
“In a preliminary experiment, out of the 500 MBA expression samples obfuscated by Tigress [8] with the MBA-obfuscation option, PLASynth-SL earned 68 semi-linear MBA expressions, while gained 46 linear MBA expressions.”
Guillaume Barbu, Ward Beullens, Emmanuelle Dottax, Christophe Giraud, Agathe Houzelot, Choayun Li, Mohammad Mahzoun, Adrián Ranea, and Jianrui Xie, ECDSA White-Box Implementations: Attacks and Designs from WhibOx 2021 Contest, 2022.
“We also used Tigress [16] for both challenges to obfuscate the C source code.”
Viraj Kumar, Privately Executable Examples, 2022.
“For this study, we used the Tigress C obfuscator [19] which can transform the code in a variety of sophisticated ways.”
B Liu, Q Zheng, J Li, D Xu, An In-Place Simplification on Mixed Boolean-Arithmetic Expressions, 2022.
“Dataset 1 comprises 500 MBA samples generated by Tigress [6] with up to three variables.”
A Kurtukova, A Romanov, A Shelupanov, A Fedotova, Complex cases of source code authorship identification using a hybrid deep neural network, 2022.
“Since most modern approaches do not consider complex cases besides obfuscation, we decided to compare the three methods with the example of identifying the author of the source code that was obfuscated with the Tigress tool.”
S Alrabaee, M Debbabi, L Wang, A Survey of Binary Code Fingerprinting Approaches: Taxonomy, Methodologies, and Features, 2022.
“Existing tools such as Tigress [10] are used to complicate the source code further, with the application of the following approaches: (a) virtualization, which changes a function into an interpreter with a bytecode language specific to the function; (b) jitting, which changes a function into another that producers its machine code at runtime; and (c) dynamic, which changes a function into another that alters its machine code at runtime continuously.”
DP Pham, Leveraging side-channel signals for IoT malware classification and rootkit detection, 2022.
“Some confusion can be observed between addopaque, virtualize, and flatten, which are executed using Tigress, and indeed they share similar options.”
A.B Kurtykova, A.C Pomanov, A.A. Shelupanov, A.M. Fedotova, Identification of the author of program source code based on heterogeneous data for solving cybersecurity problems, 2022.
Russian. Compares three source-code authorship-identification methods on C code obfuscated with Tigress, reporting accuracy against the Tigress-obfuscated set.
“было решено осуществить сравнение трех методов на примере идентификации автора исходного кода программы, обфусцированного с помощью инструмента Tigress.”
Voitsechovdskij, Andrij, Valeriovich, Models and methods against malware classifiers based on machine learning, 2022.
Ukrainian, with an English abstract. Applies Tigress transformations to malware samples to evade machine-learning malware classifiers.
“Using these features, algorithms for counteraction based on elementary mutations and the use of obfuscation methods using tigress and llvm obfuscator are formulated.”
L Brötzmann, Design and Implementation of Obfuscation Techniques with Predictable Size and Execution Overhead, 2021.
“Tigress and Obfuscator LLVM are used as comparisons to see how Obfuscat performs in size overhead, runtime overhead, and precision.”
Patrick Kochberger, Sebastian Schrittwieser, Stefan Schweighofer, Peter Kieseberg, Edgar R. Weippl, SoK: Automatic Deobfuscation of Virtualization-protected Applications [ARES 2021], 2021.
“We used the Tigress command line option --Transform=Virtualize without any additional parameters (see Listing 5).”
Yujie Zhao, Zhanyong Tang, Guixin Ye, Xiaoqing Gong, Dingyi Fang, Input-Output Example-Guided Data Deobfuscation on Binary, 2021.
“To establish the ground truth about the obfuscated assembly codes, we use two open-source code obfuscation tools, OLLVM [16] and Tigress [14].”
Colby B. Parker, J. Todd McDonald and Dimitrios Damopoulos, Machine Learning Classification of Obfuscation using Image Visualization, 2021.
“The obfuscation of our samples is accomplished with the Tigress and OLLVM obfuscators.”
Anjali J Suresh; Sriram Sankaran, Power Profiling and Analysis of Code Obfuscation for Embedded Devices, 2020.
“We choose these obfuscation tools because OLLVM is platform and language independent while Tigress is platform dependent and processes C code.”
Ramtine Tofighi-Shirazi, Irina Măriuca Asăvoae, Philippe Elbaz-Vincent, Fine-Grained Static Detection of Obfuscation Transforms Using Ensemble-Learning and Semantic Reasoning, 2019.
“In order to create our models, we generate obfuscated as well as clean samples. This generation is done using publicly available obfuscators, specifically Tigress and OLLVM.”
MT Shiraz, Analysis of obfuscation transformations on binary code, 2019.
“We start by calculating for each set the similarity percentages based on 5000 samples of predicates, either normal or opaque predicates generated by the Tigress obfuscator on a dataset of C code samples (see Section 5.4.1).”
Peter Garba, Matteo Favaro, SATURN - Software Deobfuscation Framework Based On LLVM, 2019.
“The sample tigress_virtualize is protected with the tigress virtualization obfuscation pass (–Transform=Virtualize).”
Coniglio, Luigi, Combining program synthesis and symbolic execution to deobfuscate binary code, 2019.
“We validate our approach on three datasets of different levels of difficulty, consisting each of 500 randomly generated expressions obfuscated using the popular obfuscation tool Tigress.”
Regano, Leonardo, An Expert System for Automatic Software Protection, 2019.
“First, the Solution Deployer gathers all the DPI that must be applied to the source code using Tigress, which is called accordingly, and produces the protected source code files: by comparing these with the original ones, a GNU patch12 file is produced.”
Emre G\"uler Cornelius Aschermann, Ali Abbasi, and Thorsten Holz, ANTIFUZZ: Impeding Fuzzing Audits of Binary Executables, 2019.
“To demonstrate that obfuscation techniques alone do not protect an application from automatic bug finding tools, we obfuscated a dummy application (see Listing 2) with Tigress 2.2 [15] and let different fuzzers find the correct crashing input.”
Mathilde Ollivier, Sebastien Bardin, Richard Bonichon, and Jean-Yves Marion, How to Kill Symbolic Deobfuscation for Free (or: Unleashing the Potential of Path-Oriented Protections), 2019.
“We will rely on Tigress [21], a widely used tool for systematic evaluation of deobfuscation methods [5, 8, 35], to apply (nested) virtualization, a most effective obfuscation [5].”
Ramtine Tofighi-Shirazi, Irina Asăvoae, Philippe Elbaz-Vincent, Thanh-Ha Le, Defeating Opaque Predicates Statically through Machine Learning and Binary Analysis, 2019.
“We start by calculating for each set the similarity percentages based on 5000 samples of predicates, either normal or opaque predicates generated by the Tigress obfuscator on a dataset of C code samples (see Section 4.1).”
Mahin Talukder, Syed Islam, Paolo Falcarin, Analysis of Obfuscated Code with Program Slicing, 2019.
“We have generated multiple variants of the same target program by running Tigress with different combinations of parameters.”
Steven H. H. Ding, Benjamin C. M. Fung, and Philippe Charland, Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization, 2019.
“We increase the difficulty on the vulnerability search by using the Tigress obfuscator.”
Jonathan Salwan, Sébastien Bardin, Marie-Laure Potet, Symbolic Deobfuscation: From Virtualized Code Back to the Original, 2019.
“For this study we relied on an open-use source protector (Tigress) and provided scripts and results of our attack as well as some solutions of the Tigress challenge.”
Sandrine Blazy, Rémi Hutin, Formal verification of a program obfuscation based on mixed Boolean-arithmetic expressions, 2019.
“We chose to compare our obfuscator with Tigress as it operates on C as well.”
Anatoli Kalysch, Oskar Milisterfer, Mykolai Protsenko, Tilo Muller, Tackling Androids Native Library Malware with Robust, Efficient and Accurate Similarity Measures, 2018.
“The control-flow alterations were performed with Tigress, a C obfuscator by Collberg [9].”
A. Salem, GoldRusher:, A miner for rapid identification of hidden code, 2018.
“Thus, we used Tigress’s [10] RandomFuns option to generate 100 random programs that contain temporal and secret-based triggers.”
Serge Guelton; Adrien Guinet; Pierrick Brunet; Juan Manuel Martinez; Fabien Dagnat; Nicolas Szlifierski, Combining Obfuscation and Optimizations in the Real World, 2018.
“For example, one can apply the CFG flattening of O-LLVM on the source obfuscated by Tigress, named tigress_output.c in Listing 3.”
Ramtine Tofighi-Shirazi, Maria Christofi, Philippe Elbaz-Vincent, Thanh-Ha Le, DoSE: Deobfuscation based on Semantic Equivalence, 2018.
“For the evaluation, we used the Tigress obfuscator [7] which implements these opaque predicates5 .”
Fabrizio Biondi, Thomas Given-Wilson, Axel Legay, Cassius Puodziusm, Jean Quilbeuf, Tutorial: An Overview of Malware Detection and Evasion Techniques, 2018.
“For our running example, we use Tigress [7] to produce an obfuscated version of the code from Fig. 6.”
Niccolò Marastoni, Roberto Giacobazzi, Roberto Giacobazzi, Mila Dalla Preda, A Deep Learning Approach to Program Similarity, 2018.
“In particular, we use Tigress to generate the obfuscated binaries dataset from the initial C dataset.”
Dominik Picheta, Code Obfuscation for the C/C++ Language, 2018.
“The Tigress obfuscator was not able to obfuscate all the source code files, the failures were due to limitations in its parser which is based on CIL.”
William D. Holder, EVALUATING OPTIMAL PHASE ORDERING IN OBFUSCATION EXECUTIVES, 2017.
“We analyze obfuscation effectiveness of variants generated by Tigress, a dynamic virtualizing obfuscator with four transformation types.”
Thomas Rinsma, Seeing through obfuscation: interactive detection and removal of opaque predicates, attacks Tigress' opaque predicates, 2017.
“We see that there is only one case in the table where extra user-input was required, namely for Tigress’s linked-list–based opaque predicates.”
Thomas Rinsma, Seeing through obfuscation: interactive detection and removal of opaque predicates, 2017.
“Tigress and OLLVM are used in sections 5.2.1 and 5.2.2 respectively to obfuscate nine arbitrarily picked medium-sized functions from GNU ls.”
Mark Janssen,, Combining learning with fuzzing for software deobfuscation, 2016.
“We have created a program that implements a state machine and which was then obfuscated using the Tigress obfuscator.”
Pierre Graux, Désobscurcissement de programmes virtualisés, 2016.
Tsong Yueh Chen, Fei-Ching Kuo, Wenjuan Ma, Willy Susilo, Dave Towey, Jeffrey Voas, and Zhi Quan Zhou, Metamorphic Testing for Cybersecurity, 2016.
“In this study, we tested four real-world obfuscators: Cobfusc (open source); Stunnix (commercial); Tigress (free software, but not open source), and Obfuscator-LLVM (open source).”
Robin David, Sebastien Bardin, Jean-Yves Marion,, Targeting Infeasibility Questions on Obfuscated Codes to study the effectiveness of Dynamic Symbolic Execution, 2016.
“We consider two sets of programs: (1) the 5 samples used Section VI-B, obfuscated with the source-to-source obfuscator Tigress [34]; (2) 5 (randomly selected) coreutils without any obfuscation.”
Harshvardhan P. Joshi, Aravindhan Dhanasekaran and Rudra Dutta, Trading Off a Vulnerability: Does Software Obfuscation Increase the Risk of ROP, 2015.
“For Tigress obfuscator, as default we use transforms that are similar to the ones offered by Obfuscator-LLVM, namely Virtualize, Init/Add/UpdateOpaque, and Flatten transforms.”
Federico Scrinzi, Behavioral Analysis of Obfuscated Code, 2015.
“For our experiments we employed OLLVM by HEIG-VD and Tigress by the University of Arizona.”
Sebastian Banescu, Mart´ ın Ochoa and Alexander Pretschner, A Framework for Measuring Software Obfuscation Resilience Against Automated Attacks, 2015.
“We applied these implementations to several small C programs obfuscated with the Tigress virtualization obfuscator.”
Johannes Kinder, Towards Static Analysis of Virtualization-Obfuscated Binaries, WCRE'12, 2012.
The paper never names Tigress, but the virtualization-obfuscated sample it analyses was produced with it and supplied by its authors, which the acknowledgements record.
“The author would like to thank Christian Collberg and Sam Martin for generously providing the obfuscated code sample.”