{"slug": "breaking-database-lock-in-agentic-regeneration-of-storage-readers-for-databases", "title": "Breaking Database Lock-In: Agentic Regeneration of Storage Readers for Databases", "summary": "Researchers introduced Jailbreak, a system that uses large language models to regenerate database storage readers, bypassing traditional database drivers like JDBC and ODBC to read storage files directly. Jailbreak generates columnar buffers compatible with query engines such as DuckDB and Apache Spark, achieving up to 27x speedups on analytical workloads. The approach aims to break database lock-in by enabling direct access to storage files without human-engineered parsing logic.", "body_md": "# Computer Science > Databases\n\n[Submitted on 8 Jul 2026]\n\n# Title:Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass\n\n[View PDF](/pdf/2607.07696)\n\n[HTML (experimental)](https://arxiv.org/html/2607.07696v1)\n\nAbstract:Analytical workloads operating on data stored in external database systems face a fundamental bottleneck: data access is guarded entirely by the database driver, like JDBC or ODBC, forcing all reads through query execution and other driver layers that are not designed for bulk columnar analytics. We present Jailbreak, an approach that bypasses the database engine entirely by reading storage files directly and materializing data as in-memory columnar buffers. Jailbreak's key insight is that database file formats, while complex, are fully specified by their source code and documentation, artifacts that Large Language Models (LLMs) can ingest to regenerate operator-specific table reading components without human-engineered parsing logic. Jailbreak leverages LLM-assisted code synthesis for database storage decoding, turning a traditionally opaque format into a directly queryable artifact. We evaluate Jailbreak on PostgreSQL and MySQL storage files, targeting analytical snapshot scenarios common in read replicas and offline processing pipelines. The generated reader produces Apache Arrow buffers consumable directly by most of the widely known query engines, including DuckDB, Apache Spark, and GPU-accelerated frameworks such as cuDF and Spark RAPIDS. We validate correctness against JDBC/ODBC-based baselines using the TPC-H benchmark across all query results, and demonstrate significant performance improvements in end-to-end analytical throughput, achieving up to 27x speedups. Our results showcase that LLM-assisted storage reader synthesis is a viable and generalizable methodology for breaking data lock-in across database systems, with applications beyond PostgreSQL and MySQL for any system whose file format is available to the LLM from documentation or source code.\n\n## Submission history\n\nFrom: Viktor Giannakouris Salalidis [[view email](/show-email/c7dbc9c1/2607.07696)]\n\n**[v1]** Wed, 8 Jul 2026 17:55:00 UTC (236 KB)\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer\n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers\n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps\n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations\n\n*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article\n\nalphaXiv\n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers\n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub\n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub\n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face\n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast\n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower\n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender\n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/breaking-database-lock-in-agentic-regeneration-of-storage-readers-for-databases", "canonical_source": "https://arxiv.org/abs/2607.07696", "published_at": "2026-07-09 20:04:25+00:00", "updated_at": "2026-07-09 20:37:45.466715+00:00", "lang": "en", "topics": ["large-language-models", "artificial-intelligence"], "entities": ["Jailbreak", "PostgreSQL", "MySQL", "Apache Arrow", "DuckDB", "Apache Spark", "cuDF", "Spark RAPIDS"], "alternates": {"html": "https://wpnews.pro/news/breaking-database-lock-in-agentic-regeneration-of-storage-readers-for-databases", "markdown": "https://wpnews.pro/news/breaking-database-lock-in-agentic-regeneration-of-storage-readers-for-databases.md", "text": "https://wpnews.pro/news/breaking-database-lock-in-agentic-regeneration-of-storage-readers-for-databases.txt", "jsonld": "https://wpnews.pro/news/breaking-database-lock-in-agentic-regeneration-of-storage-readers-for-databases.jsonld"}}