TasmScan: Continuation-Aware Taint Analysis for TVM Bytecode with Savelist Abstraction

📅 2026-09-15
📈 Citations: 0
Influential: 0
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🤖 AI Summary
为了解决TVM字节码中跨延续数据流跟踪问题,TasmScan通过建模保存列表语义及路径敏感污点分析方法进行静态分析。
📝 Abstract
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 17x 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.
Problem

Research questions and friction points this paper is trying to address.

TVM
continuation
savelist
data flow tracking
bytecode analysis
Innovation

Methods, ideas, or system contributions that make the work stand out.

TVM
savelist abstraction
continuation-aware taint analysis
TASIR
path-sensitive
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