A game semantics framework delivers the first sound and complete open-world assertion checking for Ethereum smart contracts, implemented as YulTracer which achieves perfect recall and precision on reentrancy benchmarks and real-world exploits.
In: Proceedings of the ACM/IEEE International Conference on Automated Software Engineering, New York, NY, USA, pp
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Quantitative partial equivalence analysis quantifies behavioral differences between original and patched programs via symbolic analysis and a range-based heuristic for numerical domains.
NullAway delivers practical compile-time null safety for Java with 1.15x build overhead and zero NPEs from its unsound assumptions in evaluated production Android crash data.
RADAGAS-GPT4o achieves a 22.73% bypass rate against 10 WAFs, succeeding more against AI/ML-based firewalls than rule-based ones.
OpDiffer applies LLMs and static analysis to opcode-level differential testing of EVMs, reporting 26 previously unknown bugs across nine implementations along with coverage gains and an estimate that 7.21% of real contracts could trigger the bugs.
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Open-World Assertion Checking for Smart Contracts via Game Semantics
A game semantics framework delivers the first sound and complete open-world assertion checking for Ethereum smart contracts, implemented as YulTracer which achieves perfect recall and precision on reentrancy benchmarks and real-world exploits.
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Quantitative Symbolic Patch Impact Analysis
Quantitative partial equivalence analysis quantifies behavioral differences between original and patched programs via symbolic analysis and a range-based heuristic for numerical domains.
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NullAway: Practical Type-Based Null Safety for Java
NullAway delivers practical compile-time null safety for Java with 1.15x build overhead and zero NPEs from its unsound assumptions in evaluated production Android crash data.
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Adversarial SQL Injection Generation with LLM-Based Architectures
RADAGAS-GPT4o achieves a 22.73% bypass rate against 10 WAFs, succeeding more against AI/ML-based firewalls than rule-based ones.
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OpDiffer: LLM-Assisted Opcode-Level Differential Testing of Ethereum Virtual Machine
OpDiffer applies LLMs and static analysis to opcode-level differential testing of EVMs, reporting 26 previously unknown bugs across nine implementations along with coverage gains and an estimate that 7.21% of real contracts could trigger the bugs.