  Maintaining confidential information control in software is a persistent
security problem where failure means secrets can be revealed via program
behaviors. Information flow control techniques traditionally have been based on
static or symbolic analyses -- limited in scalability and specialized to
particular languages. When programs do leak secrets there are no approaches to
automatically repair them unless the leak causes a functional test to fail. We
present our vision for HyperGI, a genetic improvement framework tha detects,
localizes and repairs information leakage. Key elements of HyperGI include (1)
the use of two orthogonal test suites, (2) a dynamic leak detection approach
which estimates and localizes potential leaks, and (3) a repair component that
produces a candidate patch using genetic improvement. We demonstrate the
successful use of HyperGI on several programs which have no failing functional
tests. We manually examine the resulting patches and identify trade-offs and
future directions for fully realizing our vision.
