all AI news
CodeCloak: A Method for Evaluating and Mitigating Code Leakage by LLM Code Assistants
April 16, 2024, 4:43 a.m. | Amit Finkman, Eden Bar-Kochva, Avishag Shapira, Dudu Mimran, Yuval Elovici, Asaf Shabtai
cs.LG updates on arXiv.org arxiv.org
Abstract: LLM-based code assistants are becoming increasingly popular among developers. These tools help developers improve their coding efficiency and reduce errors by providing real-time suggestions based on the developer's codebase. While beneficial, these tools might inadvertently expose the developer's proprietary code to the code assistant service provider during the development process. In this work, we propose two complementary methods to mitigate the risk of code leakage when using LLM-based code assistants. The first is a technique …
abstract arxiv assistants code codebase coding cs.cl cs.cr cs.lg cs.pl developer developers efficiency errors llm popular proprietary real-time reduce suggestions tools type
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US
Research Engineer
@ Allora Labs | Remote
Ecosystem Manager
@ Allora Labs | Remote
Founding AI Engineer, Agents
@ Occam AI | New York