Impossible to hide secret ...: Uncovering Security and Privacy Issues in LLM-native IDEs
Source
Mostafijur Rahman Akhond, Md Afif Al Mamun, Gias Uddin, Song Wang
Author
Mostafijur Rahman Akhond, Md Afif Al Mamun, Gias Uddin, Song Wang
Published
Why it matters
The agentic IDE holding your credentials and source is itself a vulnerability surface.
arXiv:2607.26390v2 Announce Type: new Abstract: LLM-native IDEs (Integrated Development Environments), aka LIDEs, are designed from the ground up to work with Large Language Models (LLMs).
LIDEs have found remarkable success in Software Engineering (SE) tasks such as coding, debugging, and program comprehension.
LIDEs are software systems, and, like any system, they can exhibit vulnerabilities.
In this paper, we study the security and privacy issues that developers reported while using popular L
Transcript
arXiv:2607.26390v2 Announce Type: new Abstract: LLM-native IDEs (Integrated Development Environments), aka LIDEs, are designed from the ground up to work with Large Language Models (LLMs). LIDEs have found remarkable success in Software Engineering (SE) tasks such as coding, debugging, and program comprehension. LIDEs are software systems, and, like any system, they can exhibit vulnerabilities. In this paper, we study the security and privacy issues that developers reported while using popular LIDEs in their development tasks. We collected 1.1M posts from 29 popular subreddits related to LIDEs. We identified 446 posts and analyzed over 6K comments to the posts that discussed security and privacy issues in almost all popular LIDEs, such as Cursor, Copilot, Codex, etc. Using a mix of qualitative and quantitative methods, we constructed a taxonomy of the reported security and privacy issues. Our results show that most issues in LIDEs stem from system-level design choices, rather than the underlying LLMs, such as user data access, unchecked autonomous actions, etc. To overcome these issues, developers frequently relied on external safeguards like code sandboxing and manual reviewing, highlighting prevalent mistrust among developers about LIDEs. We share lessons from our study to support future design of secure and privacy-aware LIDEs.
