Xiaoao Zhu, Ni Zhang, Jie Ren, Zhiqiang Li, Jie Zheng, Zhanyong Tang, Zheng Wang · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30877 · open-access PDF
将被删除的、高度优化的二进制转化为 Canon,预优化LLVM中间表示(IR)对于下游的安全分析和遗传软件迁移至不可或缺的情况下至关重要。然而,积极的编译器优化重写控制和数据流,删除结构性元数据,并引入类型删除的低级别语法,导致当前的升降器产生错误的或语义分散的IR。
Machine translation (Meta M2M100); the English original follows.
Lifting stripped, heavily optimized binaries to a canonical, pre-optimization LLVM intermediate representation (IR) is essential for downstream security analysis and legacy software migration when source code is unavailable. However, aggressive compiler optimizations rewrite control and data flow, remove structural metadata, and introduce type-erased low-level idioms, causing current lifters to produce malformed or semantically divergent IR. We present ATLAS, a multi-agent framework designed to reconstruct formally valid and canonical LLVM IR from optimized disassembly.ATLAS combines (i) Struc
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