InterType: Breaking the Single-Function Barrier in Neural Binary Type Inference
A cross-function Transformer approach to binary type inference improves variable recovery in stripped executables, advancing automated vulnerability discovery and malware analysis capabilities.
Summary written by editorial AI · Source link below
arXiv:2610.26906v1 Announce Type: new Abstract: Variable type recovery is a critical task in binary reverse engineering, enabling downstream applications such as vulnerability discovery, malware analysis, and security enforcement. While recent Transformer-based models have demonstrated strong performance in type inference by learning instruction-level semantics, they predominantly operate within single-function boundaries (i.e. intra-procedural analysis), ignoring the rich type constraints that
Editorial Analysis
Better automated type recovery in stripped binaries accelerates vulnerability discovery and malware reverse engineering — reducing the manual effort in security research workflows.
Assess whether InterType's cross-function approach could complement your existing binary analysis tools for faster vulnerability triage.
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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