Data Randomization


Attacks that exploit memory errors are still a serious problem. We present data randomization, a new technique that provides probabilistic protection against these attacks by xoring data with random masks. Data randomization uses static analysis to partition instruction operands into equivalence classes: it places two operands in the same class if they may refer to the same object in an execution that does not violate memory safety. Then it assigns a random mask to each class and it generates code instrumented to xor data read from or written to memory with the mask of the memory operand’s class. Therefore, attacks that violate the results of the static analysis have unpredictable results. We implemented a data randomization prototype that compiles programs without modifications and can prevent many attacks with low overhead. Our prototype prevents all the attacks in our benchmarks while introducing an average runtime overhead of 11% (0% to 27%) and an average space overhead below 1%.