The Integer-Overflow-to-Buffer-Overflow(IO2BO)vulnerability has been widely exploited by attackers to cause severe damages to computer systems.Automatically identifying this kind of vulnerability is critical for softw...The Integer-Overflow-to-Buffer-Overflow(IO2BO)vulnerability has been widely exploited by attackers to cause severe damages to computer systems.Automatically identifying this kind of vulnerability is critical for software security.Despite many works have been done to mitigate integer overflow,existing tools either report large number of false positives or introduce unacceptable time consumption.To address this problem,in this article we present a static analysis framework.It first constructs an inter-procedural call graph and utilizes taint analysis to accurately identify potential IO2BO vulnerabilities.Then it uses a light-weight method to further filter out false positives.Specifically,it generates constraints representing the conditions under which a potential IO2BO vulnerability can be triggered,and feeds the constraints to SMT solver to decide their satisfiability.We have implemented a prototype system ELAID based on LLVM,and evaluated it on 228 programs of the NIST’s SAMATE Juliet test suite and 14 known IO2BO vulnerabilities in real world.The experiment results show that our system can effectively and efficiently detect all known IO2BO vulnerabilities.展开更多
The Integer-Overflow-to-Buffer-Overflow(IO2BO)vulnerability has been widely exploited by attackers to cause severe damages to computer systems.Automatically identifying this kind of vulnerability is critical for softw...The Integer-Overflow-to-Buffer-Overflow(IO2BO)vulnerability has been widely exploited by attackers to cause severe damages to computer systems.Automatically identifying this kind of vulnerability is critical for software security.Despite many works have been done to mitigate integer overflow,existing tools either report large number of false positives or introduce unacceptable time consumption.To address this problem,in this article we present a static analysis framework.It first constructs an inter-procedural call graph and utilizes taint analysis to accurately identify potential IO2BO vulnerabilities.Then it uses a light-weight method to further filter out false positives.Specifically,it generates constraints representing the conditions under which a potential IO2BO vulnerability can be triggered,and feeds the constraints to SMT solver to decide their satisfiability.We have implemented a prototype system ELAID based on LLVM,and evaluated it on 228 programs of the NIST’s SAMATE Juliet test suite and 14 known IO2BO vulnerabilities in real world.The experiment results show that our system can effectively and efficiently detect all known IO2BO vulnerabilities.展开更多
基金This research was supported in part by the National Natural Science Foundation of China(Grant No.61802394,U1836209)Foundation of Science and Technology on Information Assurance Laboratory(No.KJ-17-110)+1 种基金National Key Research and Development Program of China(2016QY071405)Strategic Priority Research Program of the CAS(XDC02040100,XDC02030200,XDC02020200).
文摘The Integer-Overflow-to-Buffer-Overflow(IO2BO)vulnerability has been widely exploited by attackers to cause severe damages to computer systems.Automatically identifying this kind of vulnerability is critical for software security.Despite many works have been done to mitigate integer overflow,existing tools either report large number of false positives or introduce unacceptable time consumption.To address this problem,in this article we present a static analysis framework.It first constructs an inter-procedural call graph and utilizes taint analysis to accurately identify potential IO2BO vulnerabilities.Then it uses a light-weight method to further filter out false positives.Specifically,it generates constraints representing the conditions under which a potential IO2BO vulnerability can be triggered,and feeds the constraints to SMT solver to decide their satisfiability.We have implemented a prototype system ELAID based on LLVM,and evaluated it on 228 programs of the NIST’s SAMATE Juliet test suite and 14 known IO2BO vulnerabilities in real world.The experiment results show that our system can effectively and efficiently detect all known IO2BO vulnerabilities.
基金supported in part by the National Natural Science Foundation of China(Grant No.61802394,U1836209)Foundation of Science and Technology on Information Assurance Laboratory(No.KJ-17-110)+1 种基金National Key Research and Development Program of China(2016QY071405)Strategic Priority Research Program of the CAS(XDC02040100,XDC02030200,XDC02020200).
文摘The Integer-Overflow-to-Buffer-Overflow(IO2BO)vulnerability has been widely exploited by attackers to cause severe damages to computer systems.Automatically identifying this kind of vulnerability is critical for software security.Despite many works have been done to mitigate integer overflow,existing tools either report large number of false positives or introduce unacceptable time consumption.To address this problem,in this article we present a static analysis framework.It first constructs an inter-procedural call graph and utilizes taint analysis to accurately identify potential IO2BO vulnerabilities.Then it uses a light-weight method to further filter out false positives.Specifically,it generates constraints representing the conditions under which a potential IO2BO vulnerability can be triggered,and feeds the constraints to SMT solver to decide their satisfiability.We have implemented a prototype system ELAID based on LLVM,and evaluated it on 228 programs of the NIST’s SAMATE Juliet test suite and 14 known IO2BO vulnerabilities in real world.The experiment results show that our system can effectively and efficiently detect all known IO2BO vulnerabilities.
