ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment

ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment | Signal Canvas | ScienceToStartup

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Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-03-31
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 86
Source count: 3
Coverage: 67%

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Evidence Receipt

Route status: building

Claims: 8

References: 86

Proof: Verification pending

Freshness state: computing

Source paper: ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment

PDF: https://arxiv.org/pdf/2603.28128v1

Source count: 3

Coverage: 67%

Last proof check: 2026-03-31T20:30:20.275Z

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Watch and verify: ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment

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Subject: ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
Experiments on large-scale datasets demonstrate that ORACAL achieves state-of-the-art performance, outperforming MANDO-HGT, MTVHunter, GNN-SC, and SCVHunter by up to 39.6 percentage points, with a peak Macro F1 of 91.28% on the primary benchmark.
Implicationpartial
Explicitly stated numeric result in the abstract with clear comparison to other methods.
Verificationpartial
partial
Evidencepartial
ORACAL maintains strong generalization on out-of-distribution datasets with 91.8% on CGT Weakness and 77.1% on DAppScan.
Implicationpartial
Directly stated numeric results for specific datasets in the abstract.
Verificationpartial
partial
Evidencepartial
Under adversarial attacks, ORACAL limits performance degradation to approximately 2.35% F1 decrease with an Attack Success Rate (ASR) of only 3%, surpassing SCVHunter and MANDO-HGT which exhibit ASRs ranging from 10.91% to 18.73%.
Implicationpartial
Specific numeric results for adversarial robustness are provided in the abstract, with a direct comparison to other methods.
Verificationpartial
partial
Evidencepartial
We propose a novel method to construct a heterogeneous graph (combining CFG, DFG, and Call Graph) and enrich its critical subgraphs using an LLM-based RAG pipeline. This injects expert-level security knowledge directly into the graph structure
Implicationpartial
This is a core methodological contribution explicitly stated in the key contributions section.
Verificationpartial
partial
Evidencepartial
ORACAL selectively enriches critical subgraphs with expert-level security context from Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs), and employs a causal attention mechanism to disentangle true vulnerability indicators from spurious correlations.
Implicationpartial
Directly stated in the abstract as a core component of the framework.
Verificationpartial
partial
Evidencepartial
For transparency, the framework adopts PGExplainer to generate subgraph-level explanations identifying vulnerability triggering paths.
Implicationpartial
Explicitly stated as a key feature in both the abstract and the methodology description.
Verificationpartial
partial
Evidencepartial
In explainability evaluation, PGExplainer achieves 32.51% Mean Intersection over Union (MIoU) against manually annotated vulnerability triggering paths.
Implicationpartial
Specific numeric result for explainability performance is provided in the abstract.
Verificationpartial
partial
Evidencepartial
Nevertheless, their detection capability is inherently bounded by handcrafted rules and predefined signatures. As a result, they primarily identify known vulnerability patterns and struggle to generalize to unseen or evolving attack strategies.
Implicationpartial
Directly stated as a limitation of prior work, providing motivation for ORACAL.
Verificationpartial
partial

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ORACAL: A Robust and Explainable Multimodal Framework for Smart Contract Vulnerability Detection with Causal Graph Enrichment

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