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Matthew Blanco

dblp:337/0584 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Program analysis · 87% Runtime systems and virtual machines · 13%
Network and information security
1 paper
Systems and software security · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Systems and software security › information flow tracking
dynamic taint analysis
0.612022
Augur: Dynamic Taint Analysis for Asynchronous JavaScript · ASE 2022
Systems and software security › vulnerability analysis
injection vulnerability detection
0.612022
Augur: Dynamic Taint Analysis for Asynchronous JavaScript · ASE 2022
Program analysis
dynamic analysis
0.612022
Augur: Dynamic Taint Analysis for Asynchronous JavaScript · ASE 2022
Program analysis › static analysis
taint analysis
0.612022
Augur: Dynamic Taint Analysis for Asynchronous JavaScript · ASE 2022
Runtime systems and virtual machines
language runtime
0.212022
Augur: Dynamic Taint Analysis for Asynchronous JavaScript · ASE 2022

Methods — techniques the papers use, named apart from their topics

abstract machine · 1.1VM-supported instrumentation · 1.1
YearPublicationVenuePosition
2022 Augur: Dynamic Taint Analysis for Asynchronous JavaScript
abstract
Dynamic taint analysis (DTA) is a popular approach to help protect JavaScript applications against injection vulnerabilities. In 2016, the ECMAScript 7 JavaScript language standard introduced many language features that most existing DTA tools for JavaScript do not support, e.g., the async/await keywords for asynchronous programming. We present Augur, a high-performance dynamic taint analysis for ES7 JavaScript that leverages VM-supported instrumentation. Integrating directly with a public, stable instrumentation API gives Augur the ability to run with high performance inside the VM and remain resilient to language revisions. We extend the abstract-machine approach to DTA to handle asynchronous function calls. In addition to providing the classic DTA use case of injection vulnerability detection, Augur is highly configurable to support any type of taint analysis, making it useful outside of the security domain. We evaluated Augur on a set of 20 benchmarks, and observed a median runtime overhead of only 1.77 ×, a median performance improvement of 298% compared to the previous state-of-the-art.
Mark W. Aldrich, Alexi Turcotte, Matthew Blanco, Frank Tip
ASE3