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)
Incoming Non-self Citations Over Time
Authors
- 1. Rui Yang
- 2. Evgenios M. Kornaropoulos
- 3. Yue Cheng
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,902 | Towards Systematic Index Dynamization | 2024 | VLDB | 4.2539423e-05 |
| 10,038 | Understanding Robustness Issues of Updatable Learned Indexes: [Experiments & Analysis] | 2026 | SIGMOD | 4.1905499e-05 |
| 10,087 | High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff | 2026 | SIGMOD | 4.1905499e-05 |
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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.
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