Program Vulnerability Repair via Inductive Inference

Program vulnerabilities, even when detected and reported, are not fixed immediately. The time lag between the reporting and fixing of a vulnerability causes open-source software systems to suffer from significant exposure to possible attacks. In this paper, we propose a counter-example guided inductive inference procedure over program states to define likely invariants at possible fix locations. The likely invariants are constructed via mutation over states at the fix location, which turns out to be more effective for inductive property inference, as compared to the usual greybox fuzzing over program inputs. Once such likely invariants, which we call patch invariants, are identified, we can use them to construct patches via simple patch templates. Our work assumes that only one failing input (representing the exploit) is available to start the repair process. Experiments on the VulnLoc data-set of 39 vulnerabilities, which has been curated in previous works on vulnerability repair, show the effectiveness of our repair procedure. As compared to proposed approaches for vulnerability repair such as CPR or SenX which are based on concolic and symbolic execution respectively, we can repair significantly more vulnerabilities. Our results show the potential for program repair via inductive constraint inference, as opposed to generating repair constraints via deductive/symbolic analysis of a given test-suite.