一句话:提出首个 TVM 字节码级静态分析框架,通过建模 savelist 语义实现跨续延污点分析并检测合约缺陷。
TON 智能合约运行于 TVM,TVM 使用一等续延与 savelist 跨续延管理寄存器和控制流;被 savelist 捕获的寄存器可绕过操作数栈传递数据,导致字节码级分析无法完整跟踪数据流。TasmScan 是首个无需源码的 TVM 字节码静态分析框架:以前向寄存器分析建模 savelist 语义,对精确解析的保存点与局部寄存器定义给出过近似保证;再将字节码提升为类型化 IR TASIR,进行路径敏感、上下文感知的污点分析以检测缺陷。在 2,921 个 TON 合约与 208 个标注基准上,TasmScan 以 100% 精度解析 294,546 个动态续延目标;五类缺陷召回 95.3%、精度 96.8%,抽样估计整体精度 85.8%,较符号执行基线中位加速 17×,路径分析无崩溃或超时。
The Open Network (TON), with a peak market capitalization exceeding $20 billion and over 175 million activated on-chain addresses, relies on the TVM (TON Virtual Machine) to execute smart contracts. TVM uses first-class continuations with savelists to manage control flow and register state across continuation invocations. Since savelist-captured registers allow data to flow across continuation boundaries without passing through the operand stack, bytecode-level analyses cannot construct complete data flow tracking without explicitly modeling savelist semantics. We present TasmScan, the first bytecode-level static analysis framework for TVM that enables cross-continuation data flow reasoning without requiring source code. TasmScan models savelist semantics via forward register analysis with a formal over-approximation guarantee for exact-resolved save sites and locally tracked register definitions, then lifts bytecode into TASIR, a typed intermediate representation, and performs path-sensitive taint analysis with context-aware sources to detect defects. We evaluate TasmScan on 2,921 contracts from the TON verifier registry and a labeled benchmark of 208 contracts with human-confirmed ground truth. On the full corpus, TasmScan resolves 294,546 dynamic continuation targets with 100% precision; ablation confirms that savelist propagation is essential for resolving indirect register calls that depend on cross-continuation register passing. On the benchmark, TasmScan detects 95.3% of defects across five classes with 96.8% precision. A 366-pair stratified sample from the full corpus estimates 85.8% overall precision. TasmScan offers a 17× median speedup over the state-of-the-art symbolic-execution baseline, and in the path-analysis comparison completes 100% of analyses with zero crashes or timeouts.