Content deleted Content added
Remove AFC template |
No edit summary |
||
Line 10:
Consider the function of a C program:
<syntaxhighlight lang="
void foo() {
int x = source();
if (x < MAX) {
int y = 2 * x;
sink(y);
Line 23:
[[File:CodePropertyGraph.png|700px|Code property graph of a sample C code snippet]]
== Implementations ==
'''Joern CPG.''' The original code property graph was implemented for C/C++ in 2013 at [[University of Göttingen]] as part of the open-source code analysis tool Joern<ref>{{cite web |title=Joern - A Robust Code Analysis Platform for C/C++ |url=http://www.mlsec.org/joern/index.shtml |website=www.mlsec.org}}</ref>. This original version has been discontinued and superseded by the open-source Joern Project<ref>{{cite web |title=Joern - The Bug Hunter's Workbench |url=https://joern.io |website=Joern - The Bug Hunter's Workbench |language=en}}</ref>, which provides a formal code property graph specification<ref>{{cite web |title=Code Property Graph Specification |url=http://cpg.joern.io/ |website=cpg-spec.github.io |language=en}}</ref> applicable to multiple programming languages. The project provides code property graph generators for C/C++, Java,
'''Plume CPG.''' Developed at [[Stellenbosch University]] in 2020 and sponsored by Amazon Science, the open-source Plume<ref>{{cite web |title=Plume |url=https://plume-oss.github.io/plume-docs/ |website=plume-oss.github.io}}</ref> project provides a code property graph for
'''Fraunhofer AISEC CPG.''' The [[Fraunhofer Society|Fraunhofer]] Institute for Applied and Integrated Security provides open-source code property graph generators for C/C++, Java, Golang, and Python<ref>{{cite web |title=Code Property Graph |url=https://github.com/Fraunhofer-AISEC/cpg |publisher=Fraunhofer AISEC |date=31 August 2022}}</ref>, albeit without a formal schema specification. It also provides the Cloud Property Graph<ref>{{cite journal |last1=Banse |first1=Christian |last2=Kunz |first2=Immanuel |last3=Schneider |first3=Angelika |last4=Weiss |first4=Konrad |title=Cloud Property Graph: Connecting Cloud Security Assessments with Static Code Analysis |journal=2021 IEEE 14th International Conference on Cloud Computing (CLOUD) |date=September 2021 |pages=13–19 |doi=10.1109/CLOUD53861.2021.00014}}</ref>, an extension of the code property graph concept that models details of cloud deployments.
'''Galois’ CPG for LLVM.''' Galois Inc. provides a code property graph based on the [[LLVM]] compiler<ref>{{cite web |title=The Code Property Graph — MATE 0.1.0.0 documentation |url=https://galoisinc.github.io/MATE/cpg.html |website=galoisinc.github.io}}</ref>. The graph represents code at different stages of the compilation and a mapping between these representations. It follows a custom schema that is defined in its documentation.
== Machine
Code property graphs provide the basis for several machine-learning-based approaches to vulnerability discovery. In particular, [[Graph neural network|graph neural networks]] (GNN) have been employed to derive vulnerability detectors.<ref>{{cite journal |last1=Zhou |first1=Yaqin |last2=Liu |first2=Shangqing |last3=Siow |first3=Jingkai |last4=Du |first4=Xiaoning |last5=Liu |first5=Yang |title=Devign: effective vulnerability identification by learning comprehensive program semantics via graph neural networks |journal=Proceedings of the 33rd International Conference on Neural Information Processing Systems |date=8 December 2019 |pages=10197–10207 |url=https://dl.acm.org/doi/10.5555/3454287.3455202 |publisher=Curran Associates Inc.}}</ref><ref>{{cite journal |last1=Haojie |first1=Zhang |last2=Yujun |first2=Li |last3=Yiwei |first3=Liu |last4=Nanxin |first4=Zhou |title=Vulmg: A Static Detection Solution For Source Code Vulnerabilities Based On Code Property Graph and Graph Attention Network |journal=2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) |date=December 2021 |pages=250–255 |doi=10.1109/ICCWAMTIP53232.2021.9674145}}</ref><ref>{{cite journal |last1=Zheng |first1=Weining |last2=Jiang |first2=Yuan |last3=Su |first3=Xiaohong |title=Vu1SPG: Vulnerability detection based on slice property graph representation learning |journal=2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE) |date=October 2021 |pages=457–467 |doi=10.1109/ISSRE52982.2021.00054}}</ref><ref>{{cite journal |last1=Chakraborty |first1=Saikat |last2=Krishna |first2=Rahul |last3=Ding |first3=Yangruibo |last4=Ray |first4=Baishakhi |title=Deep Learning based Vulnerability Detection: Are We There Yet |journal=IEEE Transactions on Software Engineering |date=2021 |pages=1–1 |doi=10.1109/TSE.2021.3087402}}</ref><ref>{{cite journal |last1=Zhou |first1=Li |last2=Huang |first2=Minhuan |last3=Li |first3=Yujun |last4=Nie |first4=Yuanping |last5=Li |first5=Jin |last6=Liu |first6=Yiwei |title=GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network |journal=2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC) |date=October 2021 |pages=381–388 |doi=10.1109/DSC53577.2021.00060}}</ref><ref>{{cite journal |last1=Ganz |first1=Tom |last2=Härterich |first2=Martin |last3=Warnecke |first3=Alexander |last4=Rieck |first4=Konrad |title=Explaining Graph Neural Networks for Vulnerability Discovery |journal=Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security |date=15 November 2021 |pages=145–156 |doi=10.1145/3474369.3486866}}</ref><ref>{{cite journal |last1=Duan |first1=Xu |last2=Wu |first2=Jingzheng |last3=Ji |first3=Shouling |last4=Rui |first4=Zhiqing |last5=Luo |first5=Tianyue |last6=Yang |first6=Mutian |last7=Wu |first7=Yanjun |title=VulSniper: Focus Your Attention to Shoot Fine-Grained Vulnerabilities |journal=Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence |date=August 2019 |pages=4665–4671 |doi=10.24963/ijcai.2019/648}}</ref>
| |||