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FlowTwist: efficient context-sensitive inside-out taint analysis for large codebases

Johannes Lerch, Ben Hermann, Eric Bodden, Mira Mezini
<span title="">2014</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dj7g645z6zfl5lut4iwy5walyu" style="color: black;">Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering - FSE 2014</a> </i> &nbsp;
This inside-out analysis requires a careful, context-sensitive coordination of both a backward and a forward taint analysis.  ...  In this work we thus present FlowTwist, a novel taintanalysis approach that works inside-out, i.e., tracks data flows from potentially vulnerable calls to the outer level of the API which the attacker  ...  To validate our hypothesis that the inside-out approach is faster and scales better for large codebases than a pure forward taint analysis, we apply both analyses to the confuseddeputy problem.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2635868.2635878">doi:10.1145/2635868.2635878</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/sigsoft/LerchHBM14.html">dblp:conf/sigsoft/LerchHBM14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rnqajudkcnb5rbwiisnaqwujoy">fatcat:rnqajudkcnb5rbwiisnaqwujoy</a> </span>
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