Address Space Layout Randomization (ASLR) is a defensive technique supported by many desktop and server operating systems. While smartphone vendors wish to make it available on their platforms, there are technical challenges in implementing ASLR on these devices. Pre-linking, limited processing power and restrictive update processes make it difficult to use existing ASLR implementation strategies even on the latest generation of smartphones. In this paper we introduce retouching, a mechanism for executable ASLR that requires no kernel modifications and is suitable for mobile devices. We have implemented ASLR for the Android operating system and evaluated its effectiveness and performance. In addition, we introduce crash stack analysis, a technique that uses crash reports locally on the device, or in aggregate in the cloud to reliably detect attempts to brute-force ASLR protection. We expect that retouching and crash stack analysis will become standard techniques in mobile ASLR implementations.

We study the security of embedded web servers used in consumer electronic devices, such as security cameras and photo frames, and for IT infrastructure, such as wireless access points and lights-out management systems. All the devices we examine turn out to be vulnerable to a variety of web attacks, including cross site scripting (XSS) and cross site request forgery (CSRF). In addition, we show that consumer electronics are particularly vulnerable to a nasty form of persistent XSS where a non-web channel such as NFS or SNMP is used to inject a malicious script. This script is later used to attack an unsuspecting user who connects to the device's web server. We refer to web attacks which are mounted through a non-web channel as cross channel scripting (XCS). We propose a client-side defense against certain XCS which we implement as a browser extension.

We demonstrate how simple local sensing and control rules achieve useful emergent behaviors in modular self-reconfigurable (metamorphic) robots. Our biologically inspired approach grows structures with the desired functionality even though the final shapes have some unspecified random variation. By contrast, other self-reconfiguration algorithms require an a-priori exact description of a target shape for the given task, which may be difficult when a robot operates in uncertain environments. We present and evaluate several control algorithms through simulation experiments of Proteo, a metamorphic robot system.