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ELAID: detecting integer-Overflow-to-Buffer-Overflow vulnerabilities by light-weight and accurate static analysis

机译:eLAID:通过轻量级和准确的静态分析来检测整数到缓冲区溢出漏洞

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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.
机译:攻击者已广泛利用整数到缓冲区溢出(IO2BO)漏洞,以对计算机系统造成严重损害。自动识别此类漏洞对于软件安全性至关重要。尽管已经进行了许多作品来缓解整数溢出,现有工具要么报告大量的误报或引入不可接受的时间消耗。要解决此问题,请在本文中呈现静态分析框架。它首先构建一个过程间呼叫图,并利用Taint分析来准确识别潜在的IO2BO漏洞。然后它使用轻量级方法来进一步过滤误报。具体地,它产生表示可以触发潜在的IO2BO漏洞的条件的约束,并将约束馈送到SMT求解器以决定它们的可靠性。我们已经实现了基于LLVM的原型系统,并在NIST的Samate Juliet测试套件和14个已知的IO2BO漏洞中评估了它的228个程序。实验结果表明,我们的系统可以有效且有效地检测所有已知的IO2BO漏洞。

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