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Atul Sadhu

dblp:167/7838 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

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

Software engineering, systems software and programming languages · 2

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.

Network and information security
2 papers
Web and mobile security · 64% Systems and software security · 21% Blockchain and cryptocurrency security · 16%
Software engineering, system software, and programming languages
1 paper
Software testing · 100%

Topics — the 6 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Web and mobile security › web security
cross-site scripting
0.422015
DexterJS: robust testing platform for DOM-based XSS vulnerabilities · ESEC/SIGSOFT FSE 2015
Auto-patching DOM-based XSS at scale · ESEC/SIGSOFT FSE 2015
Web and mobile security › javascript security
DOM-based XSS
0.422015
DexterJS: robust testing platform for DOM-based XSS vulnerabilities · ESEC/SIGSOFT FSE 2015
Auto-patching DOM-based XSS at scale · ESEC/SIGSOFT FSE 2015
Systems and software security › vulnerability patching
automated patching
0.212015
Auto-patching DOM-based XSS at scale · ESEC/SIGSOFT FSE 2015
Blockchain and cryptocurrency security › smart contract security
vulnerability detection
0.212015
DexterJS: robust testing platform for DOM-based XSS vulnerabilities · ESEC/SIGSOFT FSE 2015
Systems and software security
vulnerability discovery
0.112015
Auto-patching DOM-based XSS at scale · ESEC/SIGSOFT FSE 2015
Software testing › non-functional testing
security testing
0.112015
DexterJS: robust testing platform for DOM-based XSS vulnerabilities · ESEC/SIGSOFT FSE 2015

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

taint analysis · 0.4source-to-source rewriting · 0.4program transformation · 0.2dynamic analysis · 0.2
YearPublicationVenuePosition
2015 Auto-patching DOM-based XSS at scale
abstract
DOM-based cross-site scripting (XSS) is a client-side code injection vulnerability that results from unsafe dynamic code generation in JavaScript applications, and has few known practical defenses. We study dynamic code evaluation practices on nearly a quarter million URLs crawled starting from the the Alexa Top 1000 websites. Of 777,082 cases of dynamic HTML/JS code generation we observe, 13.3% use unsafe string interpolation for dynamic code generation — a well-known dangerous coding practice. To remedy this, we propose a technique to generate secure patches that replace unsafe string interpolation with safer code that utilizes programmatic DOM construction techniques. Our system transparently auto-patches the vulnerable site while incurring only 5.2 − 8.07% overhead. The patching mechanism requires no access to server-side code or modification to browsers, and thus is practical as a turnkey defense.
Inian Parameshwaran, Enrico Budianto, Shweta Shinde, Hung Dang, Atul Sadhu, Prateek Saxena
ESEC/SIGSOFT FSE5
2015 DexterJS: robust testing platform for DOM-based XSS vulnerabilities
abstract
DOM-based cross-site scripting (XSS) is a client-side vulnerability that pervades JavaScript applications on the web, and has few known practical defenses. In this paper, we introduce DEXTERJS, a testing platform for detecting and validating DOM-based XSS vulnerabilities on web applications. DEXTERJS leverages source-to source rewriting to carry out character-precise taint tracking when executing in the browser context—thus being able to identify vulnerable information flows in a web page. By scanning a web page, DEXTERJS produces working exploits that validate DOM-based XSS vulnerability on the page. DEXTERJS is robust, has been tested on Alexa’s top 1000 sites, and has found a total of 820 distinct zero-day DOM-XSS confirmed exploits automatically.
Inian Parameshwaran, Enrico Budianto, Shweta Shinde, Hung Dang, Atul Sadhu, Prateek Saxena
ESEC/SIGSOFT FSE5