Detecting Logic Vulnerabilities Across the Contract and Device Layers of Blockchain-Enabled IoT With Multi-Agent Heterogeneous Graph Attention

arXiv:2609.18344 · cs.CR · Submitted 2026-09-16 · Read on arXiv

cs.CR

Submitted: 2026-09-16

Updated: 2026-09-16

Code: https://github.com/ConsenSysDiligence/mythril

Project page: https://www.fstar-lang.org

License: http://creativecommons.org/licenses/by/4.0/

The gist: Blockchain-enabled Internet of Things (IoT) systems integrate smart contracts with embedded devices to support decentralized device management and access control.

Terminology

Accuracy — 23× in this paper · explained in 1 episode
Trace — 22× in this paper · explained in 2 episodes
Heterogeneous Graph — 13× in this paper · explained in 2 episodes
Embeddings — 12× in this paper · explained in 2 episodes
Ablation — 7× in this paper · explained in 1 episode
Analyzer — 7× in this paper · explained in 1 episode
AUC — 4× in this paper · explained in 2 episodes
Blockchain-enabled Internet of Things — 4× in this paper
Graph Construction — 4× in this paper · explained in 3 episodes
Verification — 4× in this paper · explained in 2 episodes
Benchmark — 3× in this paper · explained in 2 episodes
Fidelity — 3× in this paper · explained in 2 episodes
Generalization — 3× in this paper · explained in 13 episodes
Transformer — 3× in this paper · explained in 2 episodes
Agentic AI — 2× in this paper · explained in 1 episode
Aggregation — 2× in this paper · explained in 1 episode
Calibration — 2× in this paper · explained in 7 episodes
Cosine Similarity — 2× in this paper · explained in 3 episodes
Formal Verification — 2× in this paper · explained in 2 episodes
GNNs — 2× in this paper · explained in 2 episodes
Network and Distributed System Security Symposium — 2× in this paper
Reinforcement Learning — 2× in this paper · explained in 4 episodes
ACM International Conference on Software Engineering — 1× in this paper
Agent Architectures — 1× in this paper · explained in 1 episode
Annual Computer Security Applications Conference — 1× in this paper
Attention Mechanism — 1× in this paper · explained in 3 episodes
Conference on Automated Software Engineering — 1× in this paper
Consistency — 1× in this paper · explained in 3 episodes
Edge Deployment — 1× in this paper · explained in 2 episodes
Information Leakage — 1× in this paper · explained in 2 episodes
International Conference on Learning Representations — 1× in this paper
International Symposium on Cyberspace Safety and Security — 1× in this paper
Loss Function — 1× in this paper · explained in 2 episodes
Multi-Agent System — 1× in this paper · explained in 6 episodes
Regularization — 1× in this paper · explained in 3 episodes
Representation Learning — 1× in this paper · explained in 2 episodes
SIGSAC Conference on Computer and Communications Security — 1× in this paper
Transformer Architecture — 1× in this paper · explained in 2 episodes
Workshop on Binary Analysis Research — 1× in this paper

Abstract

Blockchain-enabled Internet of Things (IoT) systems integrate smart contracts with embedded devices to support decentralized device management and access control. Their security therefore depends jointly on the logic of on-chain contracts and off-chain device firmware. Logic flaws in either layer can violate the same system invariants, such as unauthorized access, improper state changes, or unguarded privileged operations. Existing approaches rely on contract analysis, firmware analysis, and graph-based vulnerability detection. However, these methods typically focus on a single layer or artifact and often depend on predefined vulnerability patterns, emulation fidelity, or homogeneous representations that obscure security-relevant component roles. They also lack a unified architecture that supports different security tasks while remaining deployable on resource-constrained gateways. To address these limitations, we extend MA-HGAT into a cross-layer multi-agent heterogeneous graph attention framework that models contracts, firmware artifacts, device fleets, and transaction streams with a unified four-role, nine-relation schema. Role-aligned agents exchange heterogeneous evidence through cross-attention, while graph-, link-, and node-level heads support multiple detection tasks and a role-based gateway--cloud partition enables lightweight edge inference. MA-HGAT thus provides a unified and deployable framework for detecting logic vulnerabilities across the contract and device layers of blockchain-enabled IoT systems.

Detecting Logic Vulnerabilities Across the Contract and Device Layers of Blockchain-Enabled IoT With Multi-Agent Heterogeneous Graph Attention

Terminology

Abstract

Sources

Related papers