@inproceedings{ba7561ef3a0d47338013bc711c83e30c,
title = "Compile-time detection of machine image sniping",
abstract = "Machine image sniping is a difficult-to-detect security vulnerability in cloud computing code. When programmatically initializing a machine, a developer specifies a machine image (operating system and file system). The developer should restrict the search to only those machine images which their organization controls: otherwise, an attacker can insert a similarly-named malicious image into the public database, where it might be selected instead of the image the developer intended. We present a lightweight type and effect system that detects requests to a cloud provider that are vulnerable to an image sniping attack, or proves that no vulnerable request exists in a codebase. We prototyped our type system for Java programs that initialize Amazon Web Services machines, and evaluated it on more than 500 codebases, detecting 14 vulnerable requests with only 3 false positives.",
keywords = "AMI sniping, AWS, DescribeImagesRequest, EC2, Java, Lightweight verification, Pluggable types",
author = "Martin Kellogg",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019 ; Conference date: 10-11-2019 Through 15-11-2019",
year = "2019",
month = nov,
doi = "10.1109/ASE.2019.00154",
language = "English (US)",
series = "Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1256--1258",
booktitle = "Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019",
address = "United States",
}