Biblio

Information flow analyses traditionally use the Program Dependence Graph (PDG) as a supporting data-structure. This graph relies on Ferrante et al.'s notion of control dependences to represent implicit flows of information. A limitation of this approach is that it may create O(textbarItextbar x textbarEtextbar) implicit flow edges in the PDG, where I are the instructions in a program, and E are the edges in its control flow graph. This paper shows that it is possible to compute information flow analyses using a different notion of implicit dependence, which yields a number of edges linear on the number of definitions plus uses of variables. Our algorithm computes these dependences in a single traversal of the program's dominance tree. This efficiency is possible due to a key property of programs in Static Single Assignment form: the definition of a variable dominates all its uses. Our algorithm correctly implements Hunt and Sands system of security types. Contrary to their original formulation, which required O(IxI) space and time for structured programs, we require only O(I). We have used our ideas to build FlowTracker, a tool that uncovers side-channel vulnerabilities in cryptographic algorithms. FlowTracker handles programs with over one-million assembly instructions in less than 200 seconds, and creates 24% less implicit flow edges than Ferrante et al.'s technique. FlowTracker has detected an issue in a constant-time implementation of Elliptic Curve Cryptography; it has found several time-variant constructions in OpenSSL, one issue in TrueCrypt and it has validated the isochronous behavior of the NaCl library.

A program subject to a Return-Oriented Programming (ROP) attack usually presents an execution trace with a high frequency of indirect branches. From this observation, several researchers have proposed to monitor the density of these instructions to detect ROP attacks. These techniques use universal thresholds: the density of indirect branches that characterizes an attack is the same for every application. This paper shows that universal thresholds are easy to circumvent. As an alternative, we introduce an inter-procedural semi-context-sensitive static code analysis that estimates the maximum density of indirect branches possible for a program. This analysis determines detection thresholds for each application; thus, making it more difficult for attackers to compromise programs via ROP. We have used an implementation of our technique in LLVM to find specific thresholds for the programs in SPEC CPU2006. By comparing these thresholds against actual execution traces of corresponding programs, we demonstrate the accuracy of our approach. Furthermore, our algorithm is practical: it finds an approximate solution to a theoretically undecidable problem, and handles programs with up to 700 thousand assembly instructions in 25 minutes.