Algorithmic Complexity Attacks on Dynamic Learned Indexes

Summary: First systematic study of algorithmic complexity attacks on dynamic learned index ALEX, introducing space and time ACAs that exploit gapped-array layouts, catastrophic-cost mitigation, and model mismatch. Space ACAs use a Multiple-Choice Knapsack-based insertion plan to maximize memory (triggering OOM with only hundreds of adversarial inserts); time ACAs craft pathological insertions that worsen model fit and slow runtime up to 1,641×. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13941
Venue: VLDB
Year: 2024
Pagerank: 5.5021992e-05
Overall Rank: 8,039 | 44.85%
DOI: 10.14778/3636218.3636232

Incoming Non-self Citations Over Time

Authors

1. Rui Yang (University of Virginia)
2. Evgenios M. Kornaropoulos (George Mason University)
3. Yue Cheng (University of Virginia)

BibTeX Citation

@article{yang_vldb24,
        title = {{Algorithmic Complexity Attacks on Dynamic Learned Indexes}},
        author = {Yang, Rui and Kornaropoulos, Evgenios M. and Cheng, Yue},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {4},
        pages = {780--793},
        doi = {10.14778/3636218.3636232},
        url = {https://doi.org/10.14778/3636218.3636232},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,988	Towards Systematic Index Dynamization	2024	VLDB	5.3384135e-05
9,599	Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis]	2026	SIGMOD	5.2492748e-05
10,267	Mathematical Foundations of Poisoning Attacks on Linear Regression over Cumulative Distribution Functions	2026	SIGMOD	5.093636e-05
10,378	High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff	2026	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 22 of 22 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
43	The Case for Learned Index Structures	2018	SIGMOD	0.00046060254
86	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00035316107
90	CrowdDB: Answering Queries with Crowdsourcing	2011	SIGMOD	0.00034951786
265	Human-powered Sorts and Joins	2012	VLDB	0.00022935368
447	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00018322593
477	The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds	2020	VLDB	0.00017851226
568	SageDB: A Learned Database System	2019	CIDR	0.0001641553
790	FITing-Tree: A Data-aware Index Structure	2019	SIGMOD	0.0001401445
847	Benchmarking Learned Indexes	2021	VLDB	0.0001365768
873	Learning Multi-dimensional Indexes	2020	SIGMOD	0.00013481915
1,174	Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads	2021	VLDB	0.00011817414
1,551	Updatable Learned Index with Precise Positions	2021	VLDB	0.00010381398
2,233	FINEdex: A Fine-grained Learned Index Scheme for Scalable and Concurrent Memory Systems	2022	VLDB	8.8968964e-05
2,409	Deco: A System for Declarative Crowdsourcing	2012	VLDB	8.6145078e-05
2,806	Are Updatable Learned Indexes Ready?	2022	VLDB	8.1013097e-05
2,910	APEX: A High-Performance Learned Index on Persistent Memory	2022	VLDB	7.9700885e-05
3,792	Learned Index: A Comprehensive Experimental Evaluation	2023	VLDB	7.1220982e-05
4,414	NFL: Robust Learned Index via Distribution Transformation	2022	VLDB	6.7159984e-05
4,516	The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures	2022	SIGMOD	6.6489359e-05
4,578	PLIN: A Persistent Learned Index for Non-Volatile Memory with High Performance and Instant Recovery	2023	VLDB	6.6210545e-05
5,447	FILM: a Fully Learned Index for Larger-than-Memory Databases	2023	VLDB	6.2149491e-05
7,785	Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems	2022	SIGMOD	5.5450355e-05

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