Long-Range Indirect Control-Flow Prediction in Stripped Binaries via Dual Virtual Hubs and Multi-Task Graph Learning
New graph-learning technique improves indirect control-flow recovery in stripped binaries, potentially boosting static-analysis precision for vulnerability research and malware RE.
Summary written by editorial AI · Source link below
arXiv:2609.03280v1 Announce Type: new Abstract: Recovering indirect control-flow (ICF) edges is fundamental to binary security analysis, yet existing methods struggle with long-range dependencies, isolate different ICF types, and are often evaluated under protocols vulnerable to label noise and data leakage. We present ICFlowNet, a unified framework for long-range ICF prediction in stripped binaries. ICFlowNet introduces candidate-aware Dual Virtual Hubs, a Global Code Hub and a Global Data Hub
Editorial Analysis
More accurate control-flow recovery in stripped binaries could strengthen the effectiveness of automated vulnerability discovery and malware analysis toolchains.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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