Vulnerabilities are challenging to locate and repair, especially when source code is unavailable and binary patching is required. Manual methods are time-consuming, require significant expertise, and do not scale to the rate at which new vulnerabilities are discovered. Automated methods are an attractive alternative, and we propose Partially Recompilable Decompilation (PRD) to help automate the process. PRD lifts suspect binary functions to source, available for analysis, revision, or review, and creates a patched binary using source- and binary-level techniques. Although decompilation and recompilation do not typically succeed on an entire binary, our approach does because it is limited to a few functions, such as those identified by our binary fault localization. We evaluate the assumptions underlying our approach and find that, without any grammar or compilation restrictions, up to 79% of individual functions are successfully decompiled and recompiled. In comparison, only 1.7% of the full C-binaries succeed. When recompilation succeeds, PRD produces test-equivalent binaries 93.0% of the time. We evaluate PRD in two contexts: a fully automated process incorporating source-level Automated Program Repair (APR) methods; and human-edited source-level repairs. When evaluated on DARPA Cyber Grand Challenge (CGC) binaries, we find that PRD-enabled APR tools, operating only on binaries, perform as well as, and sometimes better than full-source tools, collectively mitigating 85 of the 148 scenarios, a success rate consistent with the same tools operating with access to the entire source code. PRD achieves similar success rates as the winning CGC entries, sometimes finding higher-quality mitigations than those produced by top CGC teams. For generality, the evaluation includes two independently developed APR tools andC++, Rode0day, and real-world binaries.

Control-Flow Integrity (CFI) is an important safety property of software, particularly in embedded and safety-critical systems, where CFI violations have led to patient deaths and can render cars remotely controllable by attackers. Previous techniques for CFI may reduce the robustness of embedded and safety-critical systems, as they handle CFI violations by stopping programs. In this work, we present PCFIRE, a preventative approach to CFI that prevents the root-causes of CFI violations to allow recovery, and enables programmers to specify robust recovery actions by providing CFI via source-code safety-checks. PCFIRE's CFI can be formally proved automatically, and supports realistic features of embedded software such as hardware and I/O access. We showcase PCFIRE by providing, and automatically proving, CFI for: benchmark programs, text utilities containing I/O, and embedded programs with sensor inputs and hardware outputs on the Raspberry Pi single-board computer.