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Massachusetts Institute of Technology

Preventing CSV Injection Attacks With A Browser Extension

Abstract

dc:description.abstract

CSV injection occurs when an attacker injects malicious code into a CSV file, and this code is executed when the file is opened in a spreadsheet program. This type of attack is possible because most spreadsheet programs have a set of built-in functions that run automatically when a CSV file is opened with the spreadsheet program. Given the widespread usage of CSV files and programs that interpret those CSV files, the risk posed by such CSV injection attacks is great. In this study, I present a browser extension designed to sanitize all downloaded CSV f iles by eliminating any harmful code while preserving the integrity of benign code. The extension does this by first finding all formulas within a CSV file, and determining whether or not each one has the potential to contain malicious code. If the extension determines that a formula may be malicious, it will edit the cell containing that formula so that spreadsheet programs will interpret the cell as text, and will not execute it.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2024

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Dedhia, Ray
Advisor dc:contributor.advisor
  • Williams, John R.

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright retained by author(s)

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/153858
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/153858

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Dedhia, Ray. Preventing CSV Injection Attacks With A Browser Extension. Massachusetts Institute of Technology, 2024. https://hdl.handle.net/1721.1/153858