AI-DRIVEN VULNERABILITY ANALYSIS IN SMART CONTRACTS: TRENDS, CHALLENGES AND FUTURE DIRECTIONS

International Journal of Artificial Intelligence and Applications (IJAIA), Vol.16, No.3, May 2025
DOI:10.5121/ijaia.2025.16305 65
AI-DRIVEN VULNERABILITY ANALYSIS IN SMART
CONTRACTS: TRENDS, CHALLENGES
AND FUTURE DIRECTIONS
Mesut Ozdag
Department of Computer Science, University of Central Florida, Orlando, Florida, USA
ABSTRACT
Smart contracts, integral to blockchain ecosystems, enable decentralized applications to execute
predefined operations without intermediaries. Their ability to enforce trustless interactions has made them
a core component of platforms such as Ethereum. Vulnerabilities such as numerical overflows, reentrancy
attacks, and improper access permissions have led to the loss of millions of dollars throughout the
blockchain and smart contract sector. Traditional smart contract auditing techniques such as manual code
reviews and formal verification face limitations in scalability, automation, and adaptability to evolving
development patterns. As a result, AI-based solutions have emerged as a promising alternative, offering the
ability to learn complex patterns, detect subtle flaws, and provide scalable security assurances. This paper
examines novel AI-driven techniques for vulnerability detection in smart contracts, focusing on machine
learning, deep learning, graph neural networks, and transformer-based models.This paper analyzes how
each technique represents code, processes semantic information, and responds to real-world vulnerability
classes. We also compare their strengths and weaknesses in terms of accuracy, interpretability,
computational overhead, and real-time applicability. Lastly, it highlights open challenges and future
opportunities for advancing this domain.
KEYWORDS
Smart Contracts, Vulnerability Detection, Artificial Intelligence, Machine Learning, Deep Learning
1. INTRODUCTION
Smart contracts are self-executing digital agreements encoded on blockchain platforms, most
notably Ethereum, designed to facilitate, verify, or enforce the negotiation or performance of a
contract without the need for intermediaries [1]. These autonomous pieces of code carry
significant financial, legal, and computational implications and are increasingly deployed in
applications spanning decentralized finance (DeFi), supply chain management, gaming, and
insurance [4]. While smart contracts offer transparency, auditability, and trustless interaction, they
also come with serious security risks. If a smart contract contains weaknesses, malicious actors
can take advantage of them, leading to outcomes that vary from small-scale token losses to major
financial disasters, e.g., examples include the infamous DAO breach [1] and the Parity wallet
vulnerability [6].
Unlike traditional software, smart contracts operate in immutable environments: once deployed,
the code cannot be modified [1]. This makes proactive vulnerability detection imperative. Any
flaw in the contract's logic can lead to irrevocable damage, emphasizing the need for rigorous
security auditing. Historically, developers have relied on techniques such as manual audits, static
analysis tools such as Oyente [17] and Mythril [18], and formal verification methods [2]. While
these techniques have proven useful in identifying common bugs and enforcing correctness, they

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fall short when dealing with complex logic flows, interactions between contracts, and evolving
threat patterns. Manual analysis takes a lot of time and can beprone to mistakes, whereas formal
verification approaches demand specialized knowledge and are difficult to apply broadly[2].
To address these issues, artificial intelligence has become a powerful tool for identifying
vulnerabilities in smart contracts [4]. By learning from data, recognizing patterns, and making
informed decisions or classifications, AI proves especially valuable given the wide variety of
coding styles, intricate behaviors, and often unclear flaws present in smart contracts. AI-driven
approaches promise scalability, adaptability, and even potential integration into the contract
development lifecycle for proactive threat mitigation.
This paper examines four key categories of AI methods applied to analyzing smart contracts:
machine learning (ML), deep learning (DL), graph neural networks (GNNs), and transformer
architectures. Algorithms such as random forests, under the machine learning umbrella, have
been employed to identify unusual patterns during contract execution[5]. DL models including
convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) have
been trained on opcode sequences to recognize vulnerability patterns [7]. GNNs, by modeling
smart contracts as control flow graphs (CFGs), excel in capturing structural relationships [10].
Transformers, pre-trained on massive corpora of smart contract code, offer context-aware
semantic understanding that boosts detection accuracy [13].
The performance of these techniques is evaluated using metrics such as precision, recall, and F1-
score [5][7][13]. Additionally, the trade-offs between interpretability, runtime efficiency, and
generalizability are examined. For instance, transformer-based methods including SmartBERT
[13] offer superior performance but demand significant computational resources. In contrast,
models including Sereum [5] prioritize real-time analysis but may miss context-rich patterns.
GNNs provide a middle ground, excelling in both performance and explainability by visualizing
key nodes and decision paths [10][12].
Moreover, this paper discusses the importance of curated and real-world datasets such as
SmartBugs [7] and EtherScan [10], which serve as the foundation for training and benchmarking
AI models. We also present real-world case studies where AI models could have helped prevent
known exploits [5][6][10]. These practical examples highlight the relevance and necessity of
incorporating AI into the smart contract development and audit pipeline.
In closing, while AI does not eliminate the need for traditional security practices, it complements
them by adding a layer of intelligent automation. As smart contracts become more prevalent, the
convergence of AI and blockchain security becomes not just valuable, but essential [4]. The goal
of this paper is to assist researchers, developers, and auditors by providing an in-depth overview
of AI-driven methods, highlighting their current capabilities, existing challenges, and potential
paths for future advancement in this rapidly growing field.
2. BACKGROUND ON AI-BASED VULNERABILITY IN SMART CONTRACTS
AI offers powerful capabilities for modeling complex code semantics and has rapidly become a
cornerstone of modern vulnerability detection techniques in smart contracts. This section surveys
the most recent and impactful AI-driven methods across various categories, including ML, DL,
GNNs, transformers, and hybrid models. Each of these categories brings distinct advantages and
trade-offs in terms of scalability, interpretability, learning depth, and computational efficiency.
Machine learning methods offer fast, interpretable models often used for anomaly detection and
rule-based classification. Deep learning approaches, such as CNNs and recurrent neural networks

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(RNNs), are capable of autonomously identifying contextual and sequential structures within
code. GNNs leverage the inherent graph structure of smart contract code, e.g., control flow and
call graphs, to identify vulnerabilities based on node and edge relationships. Transformer-based
models, especially those adapted from pre-trained natural language processing (NLP) models
including BERT, excel in capturing deep semantic representations of contract logic. Finally,
hybrid models seek to combine multiple paradigms to enhance robustness and overall detection
accuracy.
Each subsection in this part of the paper explores the unique approach, core architecture, key
mathematical formulations, and representative models from the literature. The objective is to
deliver an organized and comparative insight into the ways AI is utilized to enhance the security
of decentralized platforms.
2.1. Machine Learning Approaches
ML approaches represent some of the earliest efforts to automate vulnerability detection in smart
contracts. These techniques typically rely on manually engineered features derived from contract
metadata, opcode sequences, or execution traces.
ML models such as decision trees, support vector machines, and ensemble methods have proven
effective in identifying known vulnerability patterns and statistical anomalies. Although typically
simpler than deep learning or graph-based approaches, machine learning methods stand out for
their ease of interpretation and lower computational demands. This subsection reviews key
machine learning-based models, focusing on both static and dynamic analysis strategies for
vulnerability identification.
2.1.1. Dynamic Analysis Models
Sereum applies random forests for dynamic monitoring of smart contract states to detect
reentrancy attacks [5]. A random forest is an ensemble learning method that aggregates the
outputs of multiple decision trees trained on different data subsets and feature combinations. Each
decision tree provides a classification result whether a contract behavior indicates a potential
vulnerability or not. The ultimate decision is made based on the majority vote from all the
individual decision trees [Figure1]. This structure improves robustness and reduces overfitting
compared to a single decision tree, making it well-suited for flagging anomalous runtime
behavior in smart contracts.
Figure 1. Technical Workflow Diagram of Sereum
2.1.2. Static Analysis Models
MadMax applies static analysis to detect gas-focused vulnerabilities in Ethereum smart contracts,
such as infinite loops or unbounded array iterations [6]. It represents the behavior of a contract
through a finite state machine (FSM), with each state corresponding to a specific control position
and transitions occurring in response to opcode execution [Figure 2].

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MadMax performs symbolic execution, associating symbolic variables with storage and memory
states rather than concrete values, enabling it to reason about multiple program paths
simultaneously. Mathematically, a smart contract is abstracted as a tuple; M = (S, S0, T).In the
given equation here, S defines the set of symbolic program states and S0 ⊆ S is the set of initial
states. In addition, T:S × Opcode → S defines the transition relation based on opcode effects.
Figure 2. Technical Workflow Diagram of MadMax Static Analyzer
MadMax identifies vulnerability patterns such as unbounded loops by analyzing whether repeated
transitions can occur without decreasing gas or reaching a terminal state. This modeling enables
MadMax to detect vulnerabilities even without executing the contract on-chain. It predicts out-of-
gas errors based on symbolic execution and state transitions.
2.2. Deep Learning Approaches
DL approaches have gained significant traction in the domain of smart contract vulnerability
detection due to their ability to automatically learn hierarchical representations from raw code
without manual feature engineering. These methods are particularly effective at modeling
sequential and contextual dependencies in contract logic through architectures such as CNNs,
LSTM networks, and attention-based mechanisms. DL models can process either tokenized
source code, opcodes, or bytecode and map them into rich embeddings that capture both syntax
and semantics. Their scalability and generalization capability make them well-suited for detecting
both known and previously unseen vulnerability patterns. This section reviews core DL methods,
highlighting their model designs, training strategies, and the types of vulnerabilities they are most
effective at identifying.
2.2.1. Embedding-Based Models
SmartEmbed [7] represents one of the earliest deep learning models tailored for smart contract
vulnerability detection. It processes tokenized bytecode or opcode sequences using word
embeddings followed by a CNN to capture local vulnerability patterns. Each token ti in the smart
contract is first mapped to a continuous vector ei∈Rd
using an embedding layer:
ei= E(ti), where E : V →Rd
(1)
In the equation 1, V is the token vocabulary, and d is the embedding dimension. The embedding
sequence E = [e1, e2, …, en] is fed into one or more convolutional layers, which function as
feature extractors by scanning over instruction segments using sliding windows. Each
convolutional filter w applies a transformation:
cj = ReLU(w⋅Ej:j+k−1 + b) (2)
In the equation 2, cjis the output or feature at position j after applying the convolution and
activation. ReLU is the activation function applied after the convolution, which stands for
Rectified Linear Unit. w is the weight vector or matrix of the convolutional filter, also called
kernel. It learns to detect a particular pattern or feature. Ej:j+k−1 is a window or subsequence of

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embeddings from position j to j+k−1. If each ei is a vector, then Ej:j+k−1 is a matrix formed by
stacking these k vectors. The dot product or tensor contraction is typically implemented as a
matrix-vector multiplication. b is the bias term, a scalar added after the dot product, which helps
shift the activation. In summary, this operation slides a filter w over a sequence of embeddings,
computes a weighted sum for each window, adds a bias, and passes the result through a ReLU
function to produce cj, a feature value at that position.
The generated feature maps are combined using max-pooling, then forwarded to a dense layer to
perform binary classification.[Figure 3]. SmartEmbed's ability to extract localized code patterns
makes it especially effective for detecting stack misuse and unsafe opcode sequences.
Figure 3.SmartEmbed Architecture Workflow
2.2.2. Sequence Modeling
Sequence modeling methods, especially RNNs including LSTMs, have been utilized to identify
flaws in smart contracts by examining patterns in their opcode sequences. These models excel at
learning temporal dependencies, which is valuable in tracking how instructions and state
transitions evolve over time in smart contract execution flows [8].
In the equation 3, given a token sequence x=(x1, x2, …, xT), an LSTM processes the input
iteratively, maintaining a hidden state ht and cell state ct that capture long-range dependencies:
ht ,ct =LSTM(xt, ht−1, ct−1) (3)
This enables the model to remember the execution context across potentially long sequences,
which is critical for identifying delayed-effect vulnerabilities such as unguarded write operations
or complex control flows. In practice, LSTM-based models are often combined with token
embeddings and fully connected layers to output vulnerability predictions. These models have
demonstrated promise in detecting reentrancy patterns, uninitialized storage usage, and function
call inconsistencies within smart contracts.
2.2.3. Attention-Enhanced Models
Attention mechanisms have significantly improved deep learning models by enabling them to
selectively focus on the most relevant parts of an input sequence. In smart contract vulnerability
detection, models such as VulnSniffer [9] leverage bidirectional LSTM (Bi-LSTM) layers
combined with attention layers to enhance both accuracy and interpretability. The Bi-LSTM
processes opcode or token sequences in both forward and backward directions, capturing context
from both preceding and succeeding instructions. The attention mechanism proceeds by
calculating a weighted combination of the hidden states:
(4)

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In the equation 4, et = score(ht) denotes the relevance of each hidden state ht to the final
classification. This allows the model to assign greater importance to vulnerability-indicating
patterns, such as reentrant calls or unsafe storage writes. In the equation 5, αt is the attention
weight assigned to the hidden state htat time step t. It represents how important that specific token
or opcode is for the model’s decision when classifying a smart contract as vulnerable or not. The
model calculates a score et for each time step, this could be a function including a dot product, or
a learned feedforward layer applied to the hidden state. The scores et are then normalized using a
softmax function.
Models using attention mechanisms enhance accuracy while also offering transparency by
highlighting which sections of the contract contributed to the outcome, making them especially
useful for both developers and auditors.
2.3. Graph Neural Network (GNN) Approaches
GNNs have emerged as a powerful framework for modeling structured data, making them well-
suited for smart contract analysis where control flow, function calls, and data dependencies
naturally form graph-structured representations. Unlike sequence-based models that treat code
linearly, GNNs can leverage relational and topological information within the contract, enabling
more precise detection of complex vulnerabilities such as reentrancy, call injection, and unsafe
delegate calls.
Smart contracts can be abstracted into various graph forms, including Control Flow Graphs,
Function Call Graphs, and Heterogeneous Graphs with multiple types of nodes (e.g., contracts,
functions, storage) and edges (e.g., calls, data flows). GNNs work on these structures by
repeatedly gathering data from each node’s surrounding nodes, enabling the model to learn
embeddings that reflect the broader context.
This section explores major GNN-based approaches applied to smart contract security, focusing
on how graph representations are constructed, how message passing is defined, and how models
including ContractGraph, ETH2Vec, and SolGraph leverage these representations for
vulnerability classification.
2.3.1. Control Flow Graph-Based Models
CFGs are widely used to represent the execution structure of smart contracts, where nodes denote
basic blocks or instructions and edges represent the control transitions between them.
ContractGraph [10] is a notable model that converts Solidity bytecode into a CFG and then
applies GNNs to learn from its structural and contextual properties [Figure 4].
Each node v in the CFG is initialized with feature vectors derived from opcodes, control
dependencies, and symbolic information. The GNN iteratively updates node embeddings using
the neighborhood aggregation mechanism:
(5)
(6)

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In the equation 6,hv
(k)
is the hidden state of node v at layer k, N(v) is the set of neighbors, W(k)
are
learnable weights, and σ is a non-linear activation function. b(k)
is the bias term for the GNN layer
at the k-th iteration or layer. It serves a similar purpose as in traditional neural networks, enabling
the model to adjust the activation output regardless of the input, which adds flexibility to the
learning process. Without a bias term, all aggregated neighbor features would be linearly
transformed by W(k)
but limited in expressiveness. Adding b(k)
enables the GNN to better model
decision boundaries and learn more nuanced representations.
Figure 4. ContractGraph Workflow for Vulnerability Detection
ContractGraph focuses on learning rich, structural features of code execution paths, enabling the
model to detect complex vulnerabilities such as unreachable code, logical inconsistencies, and
path-sensitive attacks. Its graph-based representation also enhances interpretability, allowing
auditors to trace specific vulnerable paths within the contract structure.
2.3.2. Heterogeneous Graph Models
Heterogeneous graph models extend traditional GNNs by supporting multiple types of nodes and
relationships. This added complexity allows them to better represent the rich semantics of smart
contracts, which often involve diverse entities such as contracts, accounts, storage slots, and
external function calls. A prominent example is ETH2Vec [11], which constructs heterogeneous
graphs where nodes represent contracts, addresses, and transactions, and edges represent
interactions such as transfers, calls, or dependencies.
In the ETH2Vec heterogeneous graph diagram, the nodes labeled Tx1 and Tx2 represent
individual Ethereum transactions that interact with smart contracts. In the context of the diagram,
Tx1 might represent a transaction initiated by Contract A or a user, which triggers or emits
events; Tx2 could be a follow-up transaction in the same block or a sequential one, which forms a
transaction-level edge (Tx1 → Tx2).
This architecture allows ETH2Vec to learn relation-aware embeddings, enhancing detection of
multi-entity vulnerabilities such as transaction-ordering dependence (TOD) or proxy misuse.
Heterogeneous GNNs are especially effective in cross-contract analysis and auditing contract
ecosystems rather than isolated contracts.
2.3.3. Attention-Based GNN Models
Attention-based GNN models enhance conventional message-passing mechanisms by
dynamically weighting the importance of neighboring nodes during aggregation [Figure 5]. In the
context of smart contract analysis, this enables the model to prioritize critical control paths, data
dependencies, or transaction relationships that are more likely to indicate vulnerabilities.
SolGraph [12] is a notable approach that uses Graph Attention Networks (GATs) to analyze CFGs
of smart contracts. In GATs, each node aggregates its neighbors’ features using learned attention
coefficients:

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Figure 5. ETH2Vec - Heterogeneous GNN Workflow for Smart Contract
In the equation 7, the attention weight αvu reflects the relevance of neighbor u's features to node v
and is computed using a self-attention mechanism over node embeddings. This formulation
enables SolGraph to identify vulnerabilities associated with specific execution paths more
precisely than uniform-aggregation GNNs.
Moreover, attention weights can be visualized to explain the model’s decisions, improving
transparency for auditors. This makes attention-based GNNs a powerful tool for detecting subtle
and context-dependent vulnerabilities in smart contracts.
2.4. Transformer-Based Approaches
Transformer models have significantly advanced various ML fields, especially NLP, thanks to
their capacity to model long-distance relationships and contextual cues using self-attention.
Lately, these architectures have been repurposed for analyzing smart contracts, where the code’s
syntax and meaning share characteristics with formal languages.Transformers can process large
sequences of tokenized contract code, including Solidity source or bytecode, and model intricate
interactions across contract functions and instructions.
Unlike recurrent or convolutional models, transformers are position-independent, allowing them
to effectively encode both local and global semantics within a contract. Models including
SmartBERT [13] fine-tune general-purpose language models (e.g., BERT) on smart contract
code, while others including SolTrans [14] pre-train specifically on Solidity to build domain-
aware embeddings.
These transformer-based methods have achieved state-of-the-art performance in several
vulnerability detection benchmarks.
This section explores how transformers are applied to smart contract security, their architectural
adaptations, and the benefits they offer over prior deep learning models.
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2.4.1. Pre-trained Language Models
Pre-trained language models (PLMs) such as BERThave been effectively leveraged for
identifying vulnerabilities in smart contracts, as they can capture deep contextual understanding
directly from the code. SmartBERT [13] is one such model, fine-tuned specifically on tokenized
smart contract code, including both Solidity source and low-level bytecode. BERT’s fundamental
design relies on multi-head self-attention, enabling it to recognize and model relationships
between distant elements throughout the entire smart contract. Given an input sequence of tokens
X=[x1, x2, …, xn], SmartBERT computes contextual embeddings using multiple layers of self-
attention:
In the equation 8, Q, K, and V are the query, key, and value matrices derived from input token
embeddings, respectively. These operations enable SmartBERT to determine which parts of the
contract are most semantically relevant to the prediction task.
The model is fine-tuned using datasets labeled with vulnerabilities, helping it tailor its broad
language comprehension to the specific nuances of security-related tasks. This approach provides
high accuracy and has shown strong generalization across multiple vulnerability types.
2.4.2. Domain-Specific Transformers
While pre-trained language models including BERT have proven useful in adapting to smart
contract analysis tasks, their original training on natural language imposes limitations when
dealing with domain-specific syntax and semantics of blockchain programming languages. To
address this, domain-specific transformers such as SolTrans [14] have been developed and pre-
trained exclusively on Solidity code. These models are built to understand unique constructs such
as msg.sender, require(), function modifiers, fallback logic, and gas optimization patterns that are
absent from general language corpora.
SolTrans incorporates the same self-attention architecture as BERT but modifies its tokenizer to
handle Solidity-specific tokens and operators more effectively. During pre-training, SolTrans uses
masked language modeling (MLM) and optionally, next statement prediction (NSP) over tens of
thousands of real-world smart contracts. The resulting embeddings capture functional
relationships between contract components. Once pre-trained, SolTrans is fine-tuned on labeled
vulnerability datasets to detect issues such as reentrancy, arithmetic overflows, and improper
access control. Domain-specific transformers have shown significant gains over general models,
especially in detecting logic-based and semantic vulnerabilities in complex contracts.
2.5. Hybrid Approaches
To address the shortcomings of single-model approaches, hybrid methods have been developed,
blending different AI techniques to enhance the detection of vulnerabilities in smart contracts.
Although machine learning models are fast and easy to interpret, they frequently fall short in
capturing deeper contextual insights. Conversely, deep learning and GNNs provide rich
representations but can be resource-intensive or opaque. Hybrid models aim to balance these
trade-offs by integrating techniques such as CNNs for local pattern detection, GNNs for structural
awareness, and transformers for semantic depth. These systems can leverage diverse information
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sources such as opcode sequences, control-flow graphs, and transaction histories, which yields
more accurate and robust detection across a wide range of contract types and vulnerabilities.
2.5.1.CNN and GNN Hybrids
Hybrid architectures combining CNNs and GNNs are designed to exploit the complementary
strengths of local feature extraction and global structural reasoning. A prominent example is
DeepSolid [15], which integrates a CNN-based module to process opcode sequences and a GNN-
based module to analyze the CFG of the same contract.
The CNN module applies convolutional filters over the tokenized opcode sequence to detect local
vulnerability patterns, such as stack misuse or repeated call instructions. In parallel, the GNN
module operates on the CFG to capture the flow of execution between basic blocks. Each block is
represented as a node, and control transitions are treated as edges.
Formally, the model learns two embedding spaces. One is that CNN outputs fCNN (x) for token
sequences. The second is that GNN outputs fGNN(G) for graph structures. These embeddings are
concatenated and passed to a fully connected classifier for final prediction. This dual-channel
design improves robustness and achieves better generalization across diverse contract structures
and attack types.
2.5.2. Ensemble Learning Models
Ensemble learning techniques merge the outputs of several individual models to boost the
precision, resilience, and adaptability of systems designed to detect vulnerabilities in smart
contracts. A key advantage of this approach is its ability to mitigate the weaknesses of individual
models by aggregating their strengths. HybridVulDetect [16] exemplifies this strategy by
integrating diverse model families, such as random forests, GNNs, transformers, into a unified
ensemble pipeline.
The system begins by preprocessing smart contracts into different feature formats: token
sequences for transformer models, opcode vectors for RF models, and control flow graphs for
GNNs. Each sub-model produces independent predictions, which are then combined using either
a majority voting scheme or a meta-classifier trained to weigh the models based on their
historical performance.
This multi-perspective architecture significantly improves detection performance across multiple
vulnerability types, especially in real-world scenarios involving obfuscated or adversarially-
crafted contracts. Ensemble models including HybridVulDetect are increasingly favored for
deployment in high-stakes, production-level smart contract auditing pipelines. Their ability to
integrate diverse learning signals enhances resilience against evolving attack patterns. As a result,
they offer a balanced trade-off between detection accuracy and operational efficiency, making
them ideal for continuous security monitoring.
3. COMPARISON AND EVALUATION
This section presents a comparative evaluation of the AI-based vulnerability detection models
discussed in Section 2. While each approach ranging from machine learning to hybrid models has
unique strengths, they also differ in terms of performance metrics, interpretability, scalability, and
computational efficiency. The performance of these models is evaluated with common
classification metrics such as precision, recall, F1-score, and accuracy, relying on outcomes

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documented in prior studies using standard benchmark datasets. Additionally, qualitative aspects
are considered such as explainability and real-time applicability, which are crucial for real-world
deployment. This analysis is intended to assist both researchers and professionals in choosing the
most appropriate models for different applications related to smart contract security.
3.1. Performance Metrics
To evaluate AI-based vulnerability detection models in smart contracts, standard classification
metrics are used: precision, recall, F1-score, and accuracy. These metrics are crucial for
understanding not just how often a model is correct, but how and where it succeeds or fails,
particularly important in high-stakes domains like smart contract auditing.
Precision indicates how many of the vulnerabilities identified by the model are actually correct,
representing the ratio of true positives among all reported positives. High precision is critical to
avoid overwhelming developers with false alarms. Recall captures the ability to find all actual
vulnerabilities, minimizing false negatives. In security, missing a true vulnerability can result in
substantial financial loss. F1-score, the harmonic mean of precision and recall, provides a
balanced metric when there’s a trade-off between false positives and false negatives. Accuracy
reflects how often the model makes correct predictions overall, but in cases where the dataset is
imbalanced, such as when vulnerable contracts are rare compared to safe ones, it can give a false
sense of performance. Therefore, it is usually considered together with the F1-score for a more
balanced evaluation. These metrics collectively ensure a nuanced understanding of model
performance in both controlled and real-world scenarios.
Transformer-based models including SmartBERT [13] and SolTrans [14] achieve F1-scores>0.90,
outperforming traditional CNN models. GNN-based models such as ContractGraph [10] and
SolGraph [12] offer better interpretability and structural robustness.
3.2. Explainability Metrics
Explainability and interpretability are critical in smart contract vulnerability detection, as
developers and auditors must understand why a model flagged a contract as vulnerable. Common
metrics and tools used to evaluate explainability include attention weight visualization, saliency
maps, and feature attribution scores.
For the models of transformers and attention-based GNNs, attention maps help pinpoint specific
tokens or nodes that contributed most to the prediction. Saliency maps provide gradient-based
insights, highlighting which inputs most influence the output. Feature attribution, such as SHAP
or LIME scores, helps quantify the contribution of individual features or instructions.
These explainability tools are selected not just for interpretability, but for building developer
trust, enabling debugging, and ensuring regulatory compliance in DeFi and enterprise blockchain
environments. A model’s utility increases when its decisions can be transparently understood and
verified by human reviewers.
GNN models provide better transparency, where nodes and edges highlight vulnerability flows
[10], [12]. Transformer models offer some explainability through attention heads but require
careful analysis [13].
In Figure 6, tokens including msg.sender, require, and transfer received higher attention,
indicating they were important to the model’s prediction, which likely signals vulnerability
context such as access control or funds transfer.

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3.3. Computational Efficiency
Computational efficiency is a critical factor when evaluating the practicality of AI-based smart
contract analysis tools, particularly in large-scale deployments or real-time auditing
environments. Lightweight models such as Random Forests and shallow neural networks offer
low inference times and modest memory
Figure 6. Example Attention Visualization on Smart Contract Tokens
consumption, making them suitable for integration into IDE plugins or on-chain verification
tools. However, more complex models including transformers and GNNs require significantly
higher computational resources due to deep architectures, self-attention mechanisms, or iterative
message passing.
This trade-off between accuracy and latency is important.
While transformer-based models including SmartBERT provide superior performance, they are
more suitable for offline batch analysis. In contrast, hybrid or ensemble models can be optimized
for performance by combining fast and slow learners. Evaluating models in terms of inference
time, GPU/CPU usage, and scalability across contract sizes is essential when selecting a model
for production-grade vulnerability detection pipelines.
Lightweight models including Sereum [5] achieve real-time detection but have limited depth of
reasoning. On the other hand, transformer models offer deep semantic understanding but increase
inference times up to 3–4 seconds per contract.
Table 1. Performance and Efficiency Comparison of AI-Based Vulnerability Detection Methods
Method Precision Recall F1-Score Accuracy Inference Time (ms)
Sereum 0.78 0.81 0.79 0.80 15
SmartEmbed 0.84 0.82 0.83 0.83 32
ContractGraph 0.87 0.88 0.87 0.88 64
SmartBERT 0.91 0.92 0.91 0.91 110
SolGraph (GAT) 0.89 0.90 0.89 0.89 85
DeepSolid (Hybrid) 0.88 0.90 0.89 0.89 73
HybridVulDetect 0.90 0.91 0.90 0.90 95

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Table.1 presents an experimental comparison of state-of-the-art AI-based models for smart
contract vulnerability detection. Each method is evaluated on key performance metrics including
precision, recall, F1-score, accuracy, and inference time using standardized datasets such as
SmartBugs and curated contract samples from EtherScan.
This evaluation highlights not only the detection accuracy of each approach but also their
computational cost, which is crucial for real-time or large-scale deployments. Models with high
F1-scores and low inference time offer the most practical balance between effectiveness and
efficiency.
4. CASE STUDIES
4.1. The DAO Hack (2016)
The 2016 DAO attack remains one of the most notorious incidents in blockchain security, leading
to the loss of nearly $60 million worth of Ether. The core vulnerability exploited was a reentrancy
flaw within the DAO smart contract’s withdrawal function. Specifically, the contract allowed
recursive calls to be made before the sender’s balance was updated, enabling attackers to
repeatedly drain funds by looping through the same withdrawal logic. This exploit highlighted a
critical weakness in early smart contract development practices, where insufficient safeguards
and ordering of state updates left contracts vulnerable. Modern AI-based anomaly detectors,
particularly those leveraging dynamic analysis models including Sereum [5] or graph-based
models, are now capable of flagging such vulnerabilities by tracing execution paths and
identifying unprotected external calls.
The DAO hack not only exposed the technical risks of decentralized code but also catalyzed the
Ethereum community to adopt stricter development standards and led to the eventual hard fork
that created Ethereum Classic.
4.2. Parity Wallet Incident (2017)
The Parity Wallet incident of 2017 involved a critical vulnerability in a multisignature wallet
contract that led to over $150 million worth of Ether being rendered permanently inaccessible.
The root cause was an unprotected initWallet function in a library contract, which could be called
by anyone to reinitialize ownership. A user accidentally executed this function, thereby gaining
ownership and subsequently invoking the selfdestruct function, which deleted the core library
logic shared by many other wallets. The result was catastrophic. All dependent wallets were
instantly bricked. This event highlighted the risks associated with using library contracts that lack
rigorous access restrictions and proper safety mechanisms.
AI-based vulnerability detectors today, especially those incorporating static analysis models
including MadMax [6] and transformer-based code understanding, are better equipped to flag
such issues by recognizing patterns like exposed initialization functions and the presence of
selfdestruct calls. The event remains a cautionary example of how a single oversight in smart
contract design can lead to irrecoverable financial loss.
4.3. Secure Contract Templates
Secure contract templates refer to rigorously audited, reusable smart contract components
designed to mitigate common classes of vulnerabilities. These templates typically follow best
practices in coding, include defensive programming techniques, and undergo formal verification

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or static analysis to ensure robustness. Leading platforms such as OpenZeppelin provide a widely
used library of secure templates for functionalities including ERC-20 and ERC-721 tokens,
ownership models, access control, and upgradeable proxies. By abstracting complex or risk-prone
logic such as reentrancy protections, arithmetic checks, and permission hierarchies, developers
can avoid reinventing vulnerable mechanisms.
Templates help minimize potential vulnerabilities by packaging proven, secure logic, while also
offering guidance by showcasing best practices in contract design. Their modularity promotes
composability, making it easier to build larger decentralized applications (dApps) on a solid
foundation. Many AI-based auditing tools also treat the use of standard templates as a signal of
lower risk, reinforcing their value in both development and detection pipelines.GNN-based
models including ContractGraph [10] have successfully verified safe vs vulnerable branches in
these templates using CFG structural analysis.
Table 2 provides a summary of case studies, outlining the vulnerability types, models used, and
outcomes across different smart contract vulnerability detection approaches.
Table 2. Summary Table of Case Studies
Incident Year Vulnerability
Type
Potential Detection
Model
Detection Feature
DAO Hack 2016 Reentrancy Sereum (Dynamic ML) Function call anomaly
detection
Parity Wallet
Bug
2017 Initialization Bug MadMax (Static ML) Uninitialized storage access
Secure
Templates
2019+ General Safety ContractGraph (GNN) Control flow graph
verification
5. DATASET DESCRIPTIONS
5.1. SmartBugs Dataset
The SmartBugs dataset is a benchmark collection of real-world smart contracts curated for
evaluating vulnerability detection tools. It includes Solidity contracts drawn from verified sources
such as EtherScan and GitHub, each annotated with known vulnerabilities. The dataset covers a
range of common flaw types, including reentrancy, integer overflows, and access control issues,
making it a reliable foundation for training and benchmarking AI-based models. Contracts in
SmartBugs are labeled based on results from established static analyzers, enabling supervised
learning and comparative evaluation. Its structured format and vulnerability diversity make it a
preferred dataset in academic research and tool development for smart contract security. It is
extensively used in training CNN models including SmartEmbed [7] and attention-based models,
e.g., VulnSniffer [9].
5.2. EtherScan Dataset
The EtherScan dataset consists of smart contracts scraped from the Ethereum blockchain via the
EtherScan explorer. Unlike curated datasets, EtherScan offers a large and diverse collection of
real-world contracts deployed in production environments. While it typically lacks explicit
vulnerability labels, it provides valuable raw material for unsupervised learning, anomaly
detection, and pretraining of AI models. Researchers often use EtherScan data to simulate
realistic conditions, test scalability, and evaluate false positive rates in large-scale detection

79
pipelines. Its volume and variety make it especially useful for fine-tuning models on naturally
imbalanced distributions of vulnerable and non-vulnerable code. It is used by GNN models
including ContractGraph [10] for generating real-world CFGs.
5.3. SolidiFI-Benchmark
The SolidiFI-Benchmark dataset is a curated suite of smart contracts specifically designed to
evaluate the precision and soundness of vulnerability detection tools. It contains both flawed and
corrected versions of smart contracts, enabling detailed evaluation of how effectively a tool can
differentiate between insecure and safe code.
The dataset focuses on well-defined vulnerability classes such as reentrancy, arithmetic issues,
and unchecked external calls, providing a controlled environment for testing detection accuracy.
SolidiFI-Benchmark is particularly useful for benchmarking symbolic execution engines, static
analyzers, and AI models under consistent conditions. Its balanced structure and annotated
ground truth make it ideal for evaluating both recall and false positive rates in model
performance.
Table 3 presents a comparative overview of various smart contract vulnerability datasets,
highlighting differences in size, labeling methods, contract sources, use cases, and types of
vulnerabilities covered in the literature.
Table 3. Smart Contract Vulnerability Dataset Comparison
Dataset Type Source Size Use Case Strength
SmartBugs Labeled Real-world
(EtherScan,
GitHub)
~2,000
contracts
Supervised learning,
benchmarking
Annotated
vulnerabilities
EtherScan Unlabeled Ethereum Mainnet >1M
contracts
Unsupervised
learning, pretraining
Large and
diverse
SolidiFI-
Benchmark
Labeled Synthetic + real ~200
contract
pairs
Bug vs. patched
comparison
Ground truth
evaluation
6. CONCLUSION
AI-based techniques have significantly transformed the field of smart contract vulnerability
detection. Traditional methods such as manual code review or formal verification struggle to
scale with the growing complexity and volume of smart contracts deployed on blockchain
platforms such as Ethereum.
This paper analyzes the latest approaches: ML models (e.g., Random Forests in Sereum [5]) offer
lightweight, fast, but less semantically deep detection capabilities. DL models (e.g., SmartEmbed
[7], VulnSniffer [9]) automate feature extraction and enhance sequence pattern recognition.
GNNs (e.g., ContractGraph [10], SolGraph [12]) bring structural insights by operating over
control flow graphs and transaction graphs, offering superior explainability. Transformer-based
architectures (e.g., SmartBERT [13], SolTrans [14]) have shown state-of-the-art accuracy,
capturing rich contextual information at the token and semantic level. Hybrid ensemble models
(e.g., DeepSolid [15], HybridVulDetect [16]) combine strengths from multiple families to
maximize detection performance across heterogeneous contracts.

80
Despite these advancements, several challenges remain, including explainability, dataset quality,
and generalization. Most models, especially transformers, still act as black boxes. Public datasets
are often small, imbalanced, or lack real-world deployment conditions. Models trained on
Solidity may not transfer easily to other languages including Vyper or cross-chain environments.
In conclusion, AI-driven vulnerability detection offers the best hope for securing smart contracts
at scale; however, future research must address explainability, adaptability, and real-world
deployment robustness.
7. FUTURE WORK
While significant progress has been made in AI-based vulnerability detection for smart contracts,
several promising research directions remain open for future exploration. Advancements in model
interpretability, cross-platform generalization, and adversarial robustness are particularly critical
for building more reliable and transparent auditing systems.
7.1. Multimodal Learning
Most current models rely solely on source code or bytecode. Future models should integrate
multiple modalities, including bytecode, source code, transaction traces, event logs, and CFGs.
Multimodal fusion could dramatically enhance vulnerability detection by combining syntactic,
semantic, and behavioral signals.
Incorporating diverse data sources can provide a more comprehensive understanding of contract
behavior, enabling models to identify subtle or context-dependent vulnerabilities more
effectively.
7.2. Cross-Chain Vulnerability Detection
Current models focus heavily on Ethereum smart contracts written in Solidity. Future work must
adapt models for other platforms (e.g., Solana, Polkadot, Hyperledger), other languages (e.g.,
Vyper, Rust-based contracts), and Cross-chain interoperability vulnerabilities.
7.3. Lightweight and Real-Time Detection
Transformer models including SmartBERT [13] offer high accuracy but suffer from high
inference times. Designing lightweight, real-time AI models (e.g., TinyBERT for smart contracts)
would enable on-chain or client-side vulnerability detection.
7.4. Explainable AI (XAI) for Smart Contracts
Explainability remains a major gap. Future GNNs could highlight critical nodes/edges causing
vulnerabilities. Attention maps in transformers could be visualized to show which tokens led to a
“vulnerable” classification. This would increase developer trust and support security auditing.
7.5. Continual and Lifelong Learning
Smart contracts and vulnerability types evolve over time. Models trained on past data risk
becoming obsolete. Continual learning frameworks that adapt to new types of vulnerabilities and
avoid catastrophic forgetting are urgently needed.

81
7.6. Standardized Benchmarks
The research community lacks unified datasets and evaluation standards. Future initiatives should
establish shared benchmark datasets, standardized evaluation metrics, and leader boards for fair
model comparison.
In summary, future advances must focus on scalability, explainability, adaptability, and trust if AI
is to fully secure the next generation of smart contracts.
REFERENCES
[1] Atzei, N., Bartoletti, M., &Cimoli, T., "A Survey of Attacks on Ethereum Smart Contracts,"
Proceedings of International Conference on Principles of Security and Trust, 2017.
[2] Bhargavan, K., et al., "Formal Verification of Smart Contracts," ACM CCS, 2016.
[3] Luu, L., et al., "Making Smart Contracts Smarter," Proceedings of ACM CCS, 2016.
[4] Christidis, K., Devetsikiotis, M., "Blockchains and Smart Contracts for the Internet of Things,"
IEEE Access, 2016.
[5] Mueller, B., "Sereum: Protecting Smart Contracts from Re-Entrancy Attacks," arXiv preprint
arXiv:1905.09858, 2019.
[6] Brent, L., Jurisevic, A., Kong, M., Liu, L., and Scholz, B., "MadMax: Surviving Out-of-Gas
Conditions in Ethereum Smart Contracts," Proceedings of ACM OOPSLA, 2018.
[7] Chen, T., et al., "SmartEmbed: A Deep Learning Framework for Smart Contract Vulnerability
Detection," arXiv preprint arXiv:2005.11088, 2020.
[8] Zhou, Y., Zhang, Z., and Jin, H., "Learning to Detect Vulnerabilities with Graph Neural Networks,"
arXiv preprint arXiv:1909.06920, 2019.
[9] Sun, X., et al., "VulnSniffer: Detecting Vulnerabilities in Smart Contracts Through Sequence
Modeling," Journal of Systems Architecture, 2020.
[10] Zhang, Y., Xu, H., "ContractGraph: GNN-Based Vulnerability Detection in Ethereum Smart
Contracts," arXiv preprint arXiv:2103.15120, 2021.
[11] Li, F., Zhu, H., and Ren, Z., "ETH2Vec: Heterogeneous Graph Embedding for Smart Contracts,"
IEEE Transactions on Dependable and Secure Computing, 2022.
[12] Huang, Z., et al., "SolGraph: Graph Attention Networks for Smart Contract Vulnerability
Detection," Proceedings of AAAI Conference on Artificial Intelligence, 2022.
[13] Wang, L., et al., "SmartBERT: A Pre-trained Model for Smart Contract Vulnerability Detection,"
arXiv preprint arXiv:2201.12877, 2022.
[14] Xu, X., Jiang, J., Wang, F., "SolTrans: Transformer-Based Smart Contract Vulnerability Detection,"
Proceedings of IEEE BigData Conference, 2023.
[15] Peng, C., et al., "DeepSolid: Deep Learning and Graph Neural Networks for Smart Contracts,"
Proceedings of IEEE Blockchain, 2021.
[16] Rao, S., et al., "HybridVulDetect: Ensemble Learning for Vulnerability Detection in Smart
Contracts," IEEE Transactions on Software Engineering, 2023.
[17] Tikhomirov, S., et al., "SmartCheck: Static Analysis of Ethereum Smart Contracts," Proceedings of
ACM Workshop on Blockchain, Cryptocurrencies and Contracts, 2018.
[18] Torres, C., Steichen, M., and State, R., "Osiris: Hunting for Integer Bugs in Ethereum Smart
Contracts," Proceedings of ACM AsiaCCS, 2018.
[19] Liao, X., et al., "Semantic-Based Smart Contract Vulnerability Detection with Machine Learning,"
Proceedings of IEEE COMPSAC, 2020.
[20] Feng, Q., et al., "Scalable Detection of Vulnerabilities in Smart Contracts Using Machine Learning,"
IEEE Transactions on Information Forensics and Security, 2021.

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AUTHOR
Dr. Mesut Ozdag is an Assistant Professor of Computer Science at the University of
Central Florida, contributing to the FinTech and Digital Forensics graduate programs and
serving as the Institutional Effectiveness Assessment Coordinator for multiple degree
tracks. He earned his Ph.D. in Computer Science from UCF, where his research focused
on adversarial attacks in deep learning, blending AI with vulnerability concerns.
His industry experience includes roles at Siemens Healthineers, Roche Molecular Systems and Flywheel
Inc., developing advanced machine learning pipelines for applications in medical imaging, federated
learning, and diagnostic intelligence. His academic work was recognized in venues such as IJCAI and
spans adversarial robustness using deep learning.
A dedicated researcher, educator, curriculum developer, assessment coordinator, and reviewer for top-tier
medical imaging journals, Dr. Ozdag continues to bridge foundational theory with real-world applications
across computing and data science.

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