Modern websites use multiple authentication cookies to allow visitors to the site different levels of access. The complexity of modern web applications can make it difficult for a web application programmer to ensure that the use of authentication cookies does not introduce vulnerabilities. Even when a programmer has access to all of the source code, this analysis can be challenging; the problem becomes even more vexing when web programmers cobble together off-the-shelf libraries to implement authentication. We have assembled a checklist for modern web programmers to verify that the cookie based authentication mechanism is securely implemented. Then, we developed a tool, Newton, to help a web application programmer to identify authentication cookies for specific parts of the website and to verify that they are securely implemented according to the checklist. We used Newton to analyze 149 sites, including the Alexa top-200 and many other popular sites across a range of categories including search, shopping, and finance. We found that 113 of them---including high-profile sites such as Yahoo, Amazon, and Fidelity---were vulnerable to hijacking attacks. Many websites have already acknowledged and fixed the vulnerabilities that we found using Newton and reported to them.

Web applications can have vulnerabilities that result in server-side data leaks. Securing sensitive data from Web applications while ensuring reasonable performance and without requiring developers to rewrite entire applications is challenging. We present SilverLine, which prevents bulk data leaks caused due to code injection in Web applications as well as compromised user-level processes on the application server. SilverLine uses login information to associate a user with each Web session; it then taints each file and database record and applies information-flow tracking to the data associated with each session to ensure that application data is released only to sessions of authorized users. SilverLine focuses on isolating data between user sessions and is thus most suitable to applications that involve single user sessions (e.g., banking, e-commerce). We have implemented SilverLine on Linux; our implementation demonstrates that SilverLine can protect a PHP-based Web application from many of the most common server-side Web application attacks by modifying only about 60 lines of code from the original application. Our evaluation shows that SilverLine incurs a performance overhead of about 20-30% over unmodified applications.

This paper surveys our ongoing work on the use of software-defined networking to simplify two acute policy problems in campus and enterprise network operations: access control and information flow control. We describe how the current coupling of high-level policy with low-level configuration makes these problems challenging today. We describe the specific policy problems faced by campus and enterprise network operators; illustrate our approach, which leverages recent trends in separating the network's “control plane” from the data plane; and show how this approach can be applied to simplify these two enterprise network management tasks. We also describe our ongoing deployment efforts to build a campus network testbed where trial designs can be deployed and evaluated. We close with a summary of current and future research challenges for solving challenges within enterprise networks within the context of this new paradigm.

We describe Trellis, a platform for hosting virtual networks on shared commodity hardware. Trellis allows each virtual network to define its own topology, control protocols, and forwarding tables, while amortizing costs by sharing the physical infrastructure. Trellis synthesizes two container-based virtualization technologies, VServer and NetNS, as well as a new tunneling mechanism, EGRE, into a coherent platform that enables high-speed virtual networks. We describe the design and implementation of Trellis and evaluate its packet-forwarding rates relative to other virtualization technologies and native kernel forwarding performance.