Algorithmic Complexity Attacks on Dynamic Learned Indexes
Pages 780 - 793
Abstract
Learned Index Structures (LIS) view a sorted index as a model that learns the data distribution, takes a data element key as input, and outputs the predicted position of the key. The original LIS can only handle lookup operations with no support for updates, rendering it impractical to use for typical workloads. To address this limitation, recent studies have focused on designing efficient dynamic learned indexes. ALEX, as the first and one of the representative dynamic learned index structures, enables dynamism by incorporating a series of design choices, including adaptive key space partitioning, dynamic model retraining, and sophisticated engineering and policies that prioritize read/write performance. While these design choices offer improved average-case performance, the emphasis on flexibility and performance increases the attack surface by allowing adversarial behaviors that maximize ALEX's memory space and time complexity in worst-case scenarios.
In this work, we present the first systematic investigation of algorithmic complexity attacks (ACAs) targeting the worst-case scenarios of ALEX. We introduce new ACAs that fall into two categories, space ACAs and time ACAs, which target the memory space and time complexity, respectively. First, our space ACA on data nodes exploits ALEX's gapped array layout and uses Multiple-Choice Knapsack (MCK) to generate an optimal adversarial insertion plan for maximizing the memory consumption at the data node level. Second, our space ACA on internal nodes exploits ALEX's catastrophic cost mitigation mechanism, causing an out-of-memory (OOM) error with only a few hundred adversarial insertions. Third, our time ACA generates pathological insertions to increase the disparity between the actual key distribution and the linear models of data nodes, deteriorating the runtime performance by up to 1, 641× compared to ALEX operating under legitimate workloads.
References
[1]
2010. Amazon Mechanical Turkon. Retrieved Decmber 10, 2023 from https://www.mturk.com/
[2]
2017. OpenStreetMap Public Data Set Now Available on AWS. Retrieved Decmber 10, 2023 from https://aws.amazon.com/about-aws/whats-new/2017/06/openstreetmap-public-data-set-now-available-on-aws/
[3]
2023. KernelDensity. Retrieved Decmber 10, 2023 from https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html
[4]
Hussam Abu-Libdeh, Deniz Altınbüken, Alex Beutel, Ed H. Chi, Lyric Pankaj Doshi, Tim Klas Kraska, Xiaozhou (Steve) Li, Andy Ly, and Chris Olston (Eds.). 2020. Learned Indexes for a Google-scale Disk-based Database. https://arxiv.org/pdf/2012.12501.pdf
[5]
Nirav Atre, Hugo Sadok, Erica Chiang, Weina Wang, and Justine Sherry. 2022. SurgeProtector: Mitigating Temporal Algorithmic Complexity Attacks Using Adversarial Scheduling. In Proceedings of the ACM SIGCOMM 2022 Conference (Amsterdam, Netherlands) (SIGCOMM '22). Association for Computing Machinery, New York, NY, USA, 723--738.
[6]
Noa Bar-Yosef and Avishai Wool. 2007. Remote Algorithmic Complexity Attacks against Randomized Hash Tables. In E-business and Telecommunications - 4th International Conference, ICETE 2007, Barcelona, Spain, July 28-31, 2007, Revised Selected Papers (Communications in Computer and Information Science), Joaquim Filipe and Mohammad S. Obaidat (Eds.), Vol. 23. Springer, 162--174.
[7]
Udi Ben-Porat, Anat Bremler-Barr, and Hanoch Levy. 2013. Vulnerability of Network Mechanisms to Sophisticated DDoS Attacks. IEEE Trans. Comput. 62, 5 (2013), 1031--1043.
[8]
Battista Biggio, Ignazio Pillai, Samuel Rota Bulò, Davide Ariu, Marcello Pelillo, and Fabio Roli. 2013. Is Data Clustering in Adversarial Settings Secure?. In Proceedings of the 2013 ACM Workshop on Artificial Intelligence and Security (Berlin, Germany) (AISec '13). Association for Computing Machinery, New York, NY, USA, 87--98.
[9]
Matthew Butrovich, Wan Shen Lim, Lin Ma, John Rollinson, William Zhang, Yu Xia, and Andrew Pavlo. 2022. Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems. In Proceedings of the 2022 International Conference on Management of Data (Philadelphia, PA, USA) (SIGMOD '22). Association for Computing Machinery, New York, NY, USA, 617--630.
[10]
Xiang Cai, Yuwei Gui, and Rob Johnson. 2009. Exploiting Unix File-System Races via Algorithmic Complexity Attacks. In 2009 30th IEEE Symposium on Security and Privacy. 27--41.
[11]
Zhichao Cao, Siying Dong, Sagar Vemuri, and David H.C. Du. 2020. Characterizing, Modeling, and Benchmarking RocksDB Key-Value Workloads at Facebook. In 18th USENIX Conference on File and Storage Technologies (FAST 20). USENIX Association, Santa Clara, CA, 209--223. https://www.usenix.org/conference/fast20/presentation/cao-zhichao
[12]
Fay Chang, Jeffrey Dean, Sanjay Ghemawat, Wilson C. Hsieh, Deborah A. Wallach, Mike Burrows, Tushar Chandra, Andrew Fikes, and Robert E. Gruber. 2006. Bigtable: A Distributed Storage System for Structured Data. In 7th USENIX Symposium on Operating Systems Design and Implementation (OSDI 06). USENIX Association, Seattle, WA. https://www.usenix.org/conference/osdi-06/bigtable-distributed-storage-system-structured-data
[13]
Brian F. Cooper, Adam Silberstein, Erwin Tam, Raghu Ramakrishnan, and Russell Sears. 2010. Benchmarking Cloud Serving Systems with YCSB. In Proceedings of the 1st ACM Symposium on Cloud Computing (Indianapolis, Indiana, USA) (SoCC '10). Association for Computing Machinery, New York, NY, USA, 143--154.
[14]
Scott A. Crosby and Dan S. Wallach. 2003. Denial of Service via Algorithmic Complexity Attacks. In 12th USENIX Security Symposium (USENIX Security 03). USENIX Association, Washington, D.C. https://www.usenix.org/conference/12th-usenix-security-symposium/denial-service-algorithmic-complexity-attacks
[15]
Levente Csikor, Dinil Mon Divakaran, Min Suk Kang, Attila Kőrösi, Balázs Sonkoly, Dávid Haja, Dimitrios P. Pezaros, Stefan Schmid, and Gábor Rétvári. 2019. Tuple Space Explosion: A Denial-of-Service Attack against a Software Packet Classifier. In Proceedings of the 15th International Conference on Emerging Networking Experiments And Technologies (Orlando, Florida) (CoNEXT '19). Association for Computing Machinery, New York, NY, USA, 292--304.
[16]
Levente Csikor, Vipul Ujawane, and Dinil Mon Divakaran. 2020. On the Feasibility and Enhancement of the Tuple Space Explosion Attack against Open vSwitch. https://arxiv.org/abs/2011.09107.
[17]
James C. Davis, Christy A. Coghlan, Francisco Servant, and Dongyoon Lee. 2018. The Impact of Regular Expression Denial of Service (ReDoS) in Practice: An Empirical Study at the Ecosystem Scale. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Lake Buena Vista, FL, USA) (ESEC/FSE 2018). Association for Computing Machinery, New York, NY, USA, 246--256.
[18]
Sarang Dharmapurikar and Vern Paxson. 2005. Robust TCP Stream Reassembly in the Presence of Adversaries. In 14th USENIX Security Symposium (USENIX Security 05). USENIX Association, Baltimore, MD. https://www.usenix.org/conference/14th-usenix-security-symposium/robust-tcp-stream-reassembly-presence-adversaries
[19]
Jialin Ding, Umar Farooq Minhas, Jia Yu, Chi Wang, Jaeyoung Do, Yinan Li, Hantian Zhang, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, David Lomet, and Tim Kraska. 2020. ALEX: An Updatable Adaptive Learned Index. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (Portland, OR, USA) (SIGMOD '20). Association for Computing Machinery, New York, NY, USA, 969--984.
[20]
Jialin Ding, Vikram Nathan, Mohammad Alizadeh, and Tim Kraska. 2020. Tsunami: A Learned Multi-Dimensional Index for Correlated Data and Skewed Workloads. Proc. VLDB Endow. 14, 2 (oct 2020), 74--86.
[21]
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong. 2020. Local Model Poisoning Attacks to Byzantine-Robust Federated Learning. In 29th USENIX Security Symposium (USENIX Security 20). USENIX Association, 1605--1622. https://www.usenix.org/conference/usenixsecurity20/presentation/fang
[22]
Paolo Ferragina and Giorgio Vinciguerra. 2020. The PGM-Index: A Fully-Dynamic Compressed Learned Index with Provable Worst-Case Bounds. Proc. VLDB Endow. 13, 8 (apr 2020), 1162--1175.
[23]
Michael J. Franklin, Donald Kossmann, Tim Kraska, Sukriti Ramesh, and Reynold Xin. 2011. CrowdDB: Answering Queries with Crowdsourcing. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data (Athens, Greece) (SIGMOD '11). Association for Computing Machinery, New York, NY, USA, 61--72.
[24]
Alex Galakatos, Michael Markovitch, Carsten Binnig, Rodrigo Fonseca, and Tim Kraska. 2019. FITing-Tree: A Data-Aware Index Structure. In Proceedings of the 2019 International Conference on Management of Data (Amsterdam, Netherlands) (SIGMOD '19). Association for Computing Machinery, New York, NY, USA, 1189--1206.
[25]
Gaston H. Gonnet. 1981. Expected Length of the Longest Probe Sequence in Hash Code Searching. J. ACM 28, 2 (apr 1981), 289--304.
[26]
Ali Hadian and Thomas Heinis. 2019. Considerations for Handling Updates in Learned Index Structures. In Proceedings of the Second International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (Amsterdam, Netherlands) (aiDM '19). Association for Computing Machinery, New York, NY, USA, Article 3, 4 pages.
[27]
Nathan Hauke and David Renardy. 2019. Denial of Service with a Fistful of Packets: Exploiting Algorithmic Complexity Vulnerabilities. Retrieved December 10, 2023 from https://www.blackhat.com/us-19/briefings/schedule/#denial-of-service-with-a-fistful-of-packets-exploiting-algorithmic-complexity-vulnerabilities-16445
[28]
Adam Jacobson and David Renardy. 2019. Algorithmic Complexity Vulnerabilities: An Introduction. Retrieved Decmber 10, 2023 from https://twosixtech.com/algorithmic-complexity-vulnerabilities-an-introduction/
[29]
Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li. 2021. Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning. arXiv:1804.00308 [cs.CR]
[30]
Andreas Kipf, Ryan Marcus, Alexander van Renen, Mihail Stoian, Alfons Kemper, Tim Kraska, and Thomas Neumann. 2019. SOSD: A Benchmark for Learned Indexes. https://arxiv.org/abs/1911.13014.
[31]
Andreas Kipf, Ryan Marcus, Alexander van Renen, Mihail Stoian, Alfons Kemper, Tim Kraska, and Thomas Neumann. 2020. RadixSpline: A Single-Pass Learned Index. In Proceedings of the Third International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (Portland, Oregon) (aiDM '20). Association for Computing Machinery, New York, NY, USA, Article 5, 5 pages.
[32]
Aniket Kittur, Boris Smus, Susheel Khamkar, and Robert E. Kraut. 2011. Crowd-Forge: Crowdsourcing Complex Work. In Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology (Santa Barbara, California, USA) (UIST '11). Association for Computing Machinery, New York, NY, USA, 43--52.
[33]
Evgenios M. Kornaropoulos, Silei Ren, and Roberto Tamassia. 2022. The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures. In Proceedings of the 2022 International Conference on Management of Data (Philadelphia, PA, USA) (SIGMOD '22). Association for Computing Machinery, New York, NY, USA, 1331--1344.
[34]
Tim Kraska, Mohammad Alizadeh, Alex Beutel, Ed H. Chi, Ani Kristo, Guillaume Leclerc, Samuel Madden, Hongzi Mao, and Vikram Nathan. 2019. SageDB: A Learned Database System. In 9th Biennial Conference on Innovative Data Systems Research, CIDR 2019, Asilomar, CA, USA, January 13-16, 2019, Online Proceedings. www.cidrdb.org. http://cidrdb.org/cidr2019/papers/p117-kraska-cidr19.pdf
[35]
Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, and Neoklis Polyzotis. 2018. The Case for Learned Index Structures. In Proceedings of the 2018 International Conference on Management of Data (Houston, TX, USA) (SIGMOD '18). Association for Computing Machinery, New York, NY, USA, 489--504.
[36]
Caroline Lemieux, Rohan Padhye, Koushik Sen, and Dawn Song. 2018. PerfFuzz: Automatically Generating Pathological Inputs. In Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis (Amsterdam, Netherlands) (ISSTA 2018). Association for Computing Machinery, New York, NY, USA, 254--265.
[37]
Pengfei Li, Yu Hua, Jingnan Jia, and Pengfei Zuo. 2021. FINEdex: A Fine-Grained Learned Index Scheme for Scalable and Concurrent Memory Systems. Proc. VLDB Endow. 15, 2 (oct 2021), 321--334.
[38]
Baotong Lu, Jialin Ding, Eric Lo, Umar Farooq Minhas, and Tianzheng Wang. 2021. APEX: A High-Performance Learned Index on Persistent Memory. Proc. VLDB Endow. 15, 3 (nov 2021), 597--610.
[39]
Chaohong Ma, Xiaohui Yu, Yifan Li, Xiaofeng Meng, and Aishan Maoliniyazi. 2022. FILM: A Fully Learned Index for Larger-Than-Memory Databases. Proc. VLDB Endow. 16, 3 (nov 2022), 561--573.
[40]
Hongzi Mao, Mohammad Alizadeh, Ishai Menache, and Srikanth Kandula. 2016. Resource Management with Deep Reinforcement Learning. In Proceedings of the 15th ACM Workshop on Hot Topics in Networks (Atlanta, GA, USA) (HotNets '16). Association for Computing Machinery, New York, NY, USA, 50--56.
[41]
Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, and Mohammad Alizadeh. 2019. Learning Scheduling Algorithms for Data Processing Clusters. In Proceedings of the ACM Special Interest Group on Data Communication (Beijing, China) (SIGCOMM '19). Association for Computing Machinery, New York, NY, USA, 270--288.
[42]
Adam Marcus, Eugene Wu, David Karger, Samuel Madden, and Robert Miller. 2011. Human-Powered Sorts and Joins. Proc. VLDB Endow. 5, 1 (sep 2011), 13--24.
[43]
Ryan Marcus, Andreas Kipf, Alexander van Renen, Mihail Stoian, Sanchit Misra, Alfons Kemper, Thomas Neumann, and Tim Kraska. 2020. Benchmarking Learned Indexes. Proc. VLDB Endow. 14, 1 (sep 2020), 1--13.
[44]
Vikram Nathan, Jialin Ding, Mohammad Alizadeh, and Tim Kraska. 2020. Learning Multi-Dimensional Indexes. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (Portland, OR, USA) (SIGMOD '20). Association for Computing Machinery, New York, NY, USA, 985--1000.
[45]
Patrick O'Neil, Edward Cheng, Dieter Gawlick, and Elizabeth O'Neil. 1996. The Log-Structured Merge-Tree (LSM-Tree). Acta Inf. 33, 4 (jun 1996), 351--385.
[46]
Hyunjung Park, Hector Garcia-Molina, Richard Pang, Neoklis Polyzotis, Aditya Parameswaran, and Jennifer Widom. 2012. Deco: A System for Declarative Crowdsourcing. Proc. VLDB Endow. 5, 12 (aug 2012), 1990--1993.
[47]
Luis Pedrosa, Rishabh Iyer, Arseniy Zaostrovnykh, Jonas Fietz, and Katerina Argyraki. 2018. Automated Synthesis of Adversarial Workloads for Network Functions. In Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication (Budapest, Hungary) (SIGCOMM '18). Association for Computing Machinery, New York, NY, USA, 372--385.
[48]
Laurent Perron and Vincent Furnon. 2023. OR-Tools. Google. Retrieved Decmber 10, 2023 from https://developers.google.com/optimization/cp/cp_solver/
[49]
Theofilos Petsios, Jason Zhao, Angelos D. Keromytis, and Suman Jana. 2017. SlowFuzz: Automated Domain-Independent Detection of Algorithmic Complexity Vulnerabilities. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (Dallas, Texas, USA) (CCS '17). Association for Computing Machinery, New York, NY, USA, 2155--2168.
[50]
Raghu Ramakrishnan and Johannes Gehrke. 2002. Database Management Systems (3 ed.). McGraw-Hill, Inc., USA.
[51]
Roei Schuster, Jin Peng Zhou, Thorsten Eisenhofer, Paul Grubbs, and Nicolas Papernot. 2023. Learned Systems Security.
[52]
Yuju Shen, Yanyan Jiang, Chang Xu, Ping Yu, Xiaoxing Ma, and Jian Lu. 2018. ReScue: Crafting Regular Expression DoS Attacks. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (Montpellier, France) (ASE '18). Association for Computing Machinery, New York, NY, USA, 225--235.
[53]
Govind Sreekar Shenoy, Jordi Tubella, and Antonio González. 2012. Improving the Resilience of an IDS against Performance Throttling Attacks. In Security and Privacy in Communication Networks - 8th International ICST Conference, SecureComm 2012, Padua, Italy, September 3-5, 2012. Revised Selected Papers (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering), Angelos D. Keromytis and Roberto Di Pietro (Eds.), Vol. 106. Springer, 167--184.
[54]
Zhenyu Song, Daniel S. Berger, Kai Li, and Wyatt Lloyd. 2020. Learning Relaxed Belady for Content Distribution Network Caching. In 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20). USENIX Association, Santa Clara, CA, 529--544. https://www.usenix.org/conference/nsdi20/presentation/song
[55]
Octavian Suciu, Radu Marginean, Yigitcan Kaya, Hal Daume III, and Tudor Dumitras. 2018. When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks. In 27th USENIX Security Symposium (USENIX Security 18). USENIX Association, Baltimore, MD, 1299--1316. https://www.usenix.org/conference/usenixsecurity18/presentation/suciu
[56]
Zhaoyan Sun, Xuanhe Zhou, and Guoliang Li. 2023. Learned Index: A Comprehensive Experimental Evaluation. Proc. VLDB Endow. 16, 8 (jun 2023), 1992--2004.
[57]
Chuzhe Tang, Zhiyuan Dong, Minjie Wang, Zhaoguo Wang, and Haibo Chen. 2019. Learned Indexes for Dynamic Workloads. https://arxiv.org/abs/1902.00655.
[58]
Juha-Matti Tilli. 2018. CVE-2018-5390: Linux Kernel TCP Reassembly Algorithm Lets Remote Users Consume Excessive CPU Resources on the Target System. Retrieved Decmber 10, 2023 from https://ubuntu.com/security/cve-2018-5390
[59]
Dana Van Aken, Andrew Pavlo, Geoffrey J. Gordon, and Bohan Zhang. 2017. Automatic Database Management System Tuning Through Large-Scale Machine Learning. In Proceedings of the 2017 ACM International Conference on Management of Data (Chicago, Illinois, USA) (SIGMOD '17). Association for Computing Machinery, New York, NY, USA, 1009--1024.
[60]
Chaichon Wongkham, Baotong Lu, Chris Liu, Zhicong Zhong, Eric Lo, and Tianzheng Wang. 2022. Are Updatable Learned Indexes Ready? Proc. VLDB Endow. 15, 11 (jul 2022), 3004--3017.
[61]
Jiacheng Wu, Yong Zhang, Shimin Chen, Jin Wang, Yu Chen, and Chunxiao Xing. 2021. Updatable Learned Index with Precise Positions. Proc. VLDB Endow. 14, 8 (apr 2021), 1276--1288.
[62]
Shangyu Wu, Yufei Cui, Jinghuan Yu, Xuan Sun, Tei-Wei Kuo, and Chun Jason Xue. 2022. NFL: Robust Learned Index via Distribution Transformation. Proc. VLDB Endow. 15, 10 (jun 2022), 2188--2200.
[63]
Valentin Wüstholz, Oswaldo Olivo, Marijn J. Heule, and Isil Dillig. 2017. Static Detection of DoS Vulnerabilities in Programs That Use Regular Expressions. In Proceedings, Part II, of the 23rd International Conference on Tools and Algorithms for the Construction and Analysis of Systems - Volume 10206. Springer-Verlag, Berlin, Heidelberg, 3--20.
[64]
Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, and Fabio Roli. 2015. Is Feature Selection Secure against Training Data Poisoning?. In Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37 (Lille, France) (ICML'15). JMLR.org, 1689--1698.
[65]
Chaofei Yang, Qing Wu, Hai Li, and Yiran Chen. 2017. Generative Poisoning Attack Method Against Neural Networks. Retrieved Decmber 10, 2023 from https://arxiv.org/abs/1703.01340
[66]
Zhou Zhang, Zhaole Chu, Peiquan Jin, Yongping Luo, Xike Xie, Shouhong Wan, Yun Luo, Xufei Wu, Peng Zou, Chunyang Zheng, Guoan Wu, and Andy Rudoff. 2022. PLIN: A Persistent Learned Index for Non-Volatile Memory with High Performance and Instant Recovery. Proc. VLDB Endow. 16, 2 (oct 2022), 243--255.
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