Taint analysis detects if data coming from a source, such as user input, flows into a sink, such as an SQL query, unsanitized (not properly escaped). Both static and dynamic taint analyses have been widely applied to detect injection vulnerabilities in real world software. A main drawback of static analysis is that it could produce false alarms. In addition, it is extremely time-consuming to manually explain the flow of tainted data from the results of the analysis, to understand why a specific warning was raised. This paper formalizes BackFlow, a context-sensitive taint flow reconstructor that, starting from the results of a taint-analysis engine, reconstructs how tainted data flows inside the program and builds paths connecting sources to sinks. BackFlow has been implemented on Julia’s static taint analysis. Experimental results on a set of standard benchmarks show that, when BackFlow produces a taint graph for an injection warning, then there is empirical evidence that such warning is a true alarm. Moreover BackFlow scales to real world programs.

BackFlow: Backward Context-Sensitive Flow Reconstruction of Taint Analysis Results

Ferrara, Pietro;Olivieri, Luca;Spoto, Fausto
2020-01-01

Abstract

Taint analysis detects if data coming from a source, such as user input, flows into a sink, such as an SQL query, unsanitized (not properly escaped). Both static and dynamic taint analyses have been widely applied to detect injection vulnerabilities in real world software. A main drawback of static analysis is that it could produce false alarms. In addition, it is extremely time-consuming to manually explain the flow of tainted data from the results of the analysis, to understand why a specific warning was raised. This paper formalizes BackFlow, a context-sensitive taint flow reconstructor that, starting from the results of a taint-analysis engine, reconstructs how tainted data flows inside the program and builds paths connecting sources to sinks. BackFlow has been implemented on Julia’s static taint analysis. Experimental results on a set of standard benchmarks show that, when BackFlow produces a taint graph for an injection warning, then there is empirical evidence that such warning is a true alarm. Moreover BackFlow scales to real world programs.
2020
978-3-030-39321-2
Static analysis, taint analysis, flow reconstruction
File in questo prodotto:
File Dimensione Formato  
Ferrara2020_Chapter_MathsfBackFlowBackwardContext-.pdf

solo utenti autorizzati

Tipologia: Versione dell'editore
Licenza: Accesso ristretto
Dimensione 1.43 MB
Formato Adobe PDF
1.43 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1009753
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 5
social impact