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Information-Flow Tracking for Web Security

Luciano Bello (Institutionen för data- och informationsteknik (Chalmers))
Göteborg : Chalmers University of Technology, 2016. ISBN: 978-91-7597-276-3.- 203 s.

The Web is evolving into a melting pot of content coming from multiple stakeholders. In this mutually distrustful setting, the combination of code and data from different providers demands new security approaches.

This thesis explores information-flow control technologies to provide security for the current Web. With focus on practicality grounded in solid theoretical foundations, we aim to fulfill the demands with respect to security, permissiveness, and flexibility.

We offer solutions for securing both the server and the client. On the server side, we suggest a taint analysis to track the information provided by the user. If the information reaches a sensitive operation without sanitization, we raise an alarm, mitigating potential exploitations. On the client side, we develop JSFlow, a JavaScript interpreter for tracking information flow in the browser. It covers the full ECMA-262 standard and browser APIs. The interpreter soundly guarantees non-interference, a policy to avoid information leaks to third-parties.

A security mechanism is only practical if it is not overly restrictive. This means that it is not enough to reject all insecure programs; an enforcement should also allow the execution of as many secure programs as possible. Permissiveness is key to reduce the number of false alarms and increase the practicality of the mechanism. This thesis pushes the limit towards more permissive sound enforcements in two approaches: a runtime hybrid system and the introduction of the value-sensitivity concept.

Finally, we study the trade-offs between security and flexibility. In some situations, non-interference is a too strong property and it can be relaxed depending on the attacker model.

The contributions go from foundational results, such as the introduction of value-sensitivity, to practical tools, like JSFlow and a Python taint-analysis library.

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Denna post skapades 2015-12-16. Senast ändrad 2016-08-15.
CPL Pubid: 228541


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