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Vulnerability Detection in AArch64 Machine Code Using a Digital Twin
US & West ·
An explainable digital twin is proposed for detecting vulnerabilities in AArch64 machine code without access to source code. The twin reproduces program execution, preserving register, memory, and allocated block states. Each instruction is transformed into a trace event, and vulnerabilities are described by symbolic rules in Kleene algebra with tests, compiled into finite automata to scan the trace without an SMT solver.
Why it matters
The novelty lies in using a digital twin for executable code analysis, extending the concept to cybersecurity and software asset management.
Original headline
Vulnerability Detection in AArch64 Machine Code Using a Digital Twin
TwinBridge builds digital twins from 3D Gaussian splatting and physics simulation to supply policy initialization and dense visual rewards for real-world online learning of a laboratory robot. Simulator task events and geometric progress yield rewards for pretraining, distilled into a visual reward model and combined with execution history during human-in-the-loop refinement.
The authors present TE-GvA, a counterfactual decision dataset for fault-driven incident response on the revised Tennessee Eastman process: 9,908 decision points from 1,990 simulated incidents, each with 4–35 candidate actions labelled via branched rollouts. With labels computed under the true hidden cause, gathering evidence is the best choice in only 1.1–1.3% of decisions.
The study shows that keeping weakly dependent or auxiliary degrees of freedom raises the dimensionality of learning and control problems, so a sparse stochastic surrogate can beat an exact high-dimensional model. It introduces a method for dependence discovery at the level of the full conditional law, combining conditional generative modeling, Gaussian-process distillation and kernel mode decomposition.