Contract-Related Rug Pull in DeFi

• Contract-related rug pulls are DeFi scams where hidden functions, such as undisclosed minting and sell restrictions, compromise protocol integrity.
• They leverage technical vulnerabilities like fake liquidity locks and ownership manipulation to expropriate funds and dilute token value.
• Detection methods include rule-based static analysis, semantic risk graphs, and formal verification to identify and mitigate smart contract risks.

A contract-related rug pull is a form of cryptoeconomic fraud in decentralized finance (DeFi) and smart contract ecosystems whereby the creator or privileged actor of a contract leverages hidden, malicious, or under-documented functions at the smart contract code level to expropriate user funds or irreversibly compromise protocol integrity. This form of rug pull is distinguished from purely transaction-manipulation scams by its reliance on source-level vulnerabilities, intentionally embedded backdoors, or protocol-level abuses of privileged access.

1. Core Mechanisms and Taxonomy

Contract-related rug pulls exploit explicit or concealed technical mechanisms within the smart contract’s logic or governance parameters. The most salient mechanisms, as systematically catalogued in multiple studies, include:

• Hidden Mint Functions: Functions not disclosed in public documentation or obscured through code-level obfuscation, enabling arbitrary token inflation by the contract owner or admin. This permits unbounded creation and transfer of tokens post-deployment, leading to catastrophic dilution of user-held assets (Lin et al., 3 Mar 2024, Sun et al., 24 Mar 2024).
• Limiting Sell Orders: Logic restricting certain addresses or all users (except administrators) from selling tokens (e.g., through togglable transfer flags or blacklist maps), effectively trapping capital in illiquid positions (Lin et al., 3 Mar 2024, Wu et al., 23 Jun 2025).
• Leaking Token/Unauthorized Transfer: Functions granting owner-access to forcefully transfer user balances, alter fees, or reroute token flows, bypassing normal permission checks (Lin et al., 3 Mar 2024, Pathade et al., 9 Jun 2025).
• Ownership Manipulation/Fake Ownership Renouncement: Code paths that retain or surreptitiously recover owner privileges even when ownership is purportedly renounced, often concealed through private variables or complex multi-step handoffs (Sun et al., 24 Mar 2024).
• Unverified Contracts: Deployment without publicly verified source code, severely limiting community and tool-based auditing and increasing the likelihood of undisclosed backdoors (Sun et al., 24 Mar 2024).
• Fake Liquidity Pool Locks: Pool "locking" routines that deceive users into believing liquidity is inaccessible, while hidden switches or logic allow privileged withdrawal at any point (Sun et al., 24 Mar 2024).

Table: Canonical Contract-Related Rug Pull Mechanisms

Mechanism	Description	Effect on Security
Hidden mint function	Owner can mint unlimited tokens	Unlimited dilution
Limiting sell orders	Users are blocked from selling	Funds are trapped
Leaking token	Privileged theft of user balances	Assets expropriated
Unverified contract	No public code/audit; hidden logic unconstrained	Undetectable backdoors
Fake LP lock	Liquidity "locked" but withdrawable by creator	Pool can be drained

These mechanisms are often deliberately obfuscated. Functions are hidden via non-obvious naming, access controls are split across delegatecall proxies, or mint operations are triggered through fallback methods rather than canonical interfaces (Pathade et al., 9 Jun 2025).

2. Detection, Formal Modeling, and Static/Dynamic Analysis

Multiple approaches have been developed for detection and formal verification of contract-related rug pull risks:

• Rule-Based Static Analysis: Tools such as CRPWarner statically analyze the EVM bytecode or Solidity source, constructing a control flow graph (CFG); domain-specific Datalog rules identify malicious functions by checking patterns such as owner-only minting without sufficient balance checks, conditional transfer restrictions, or arbitrary fee manipulations (Lin et al., 3 Mar 2024). Slither and related static analyzers are used to scan for explicit use of dangerous patterns (e.g., selfdestruct, unrestricted mint, delegatecall to untrusted addresses, external calls in loops) and weight their findings via heuristic scoring models (Pathade et al., 9 Jun 2025).
• Semantic Risk Graphs: RPHunter introduces the Semantic Risk Code Graph (SRCG), modeling detailed execution paths (basic blocks, critical edges) and annotating them with code-level rug pull risk features (mint, restrict, or siphon operations). Risk blocks (e.g., owner transfers bypassing checks) are integrated into the SRCG, which then forms the input to further GNN-based analysis (Wu et al., 23 Jun 2025).
• Transaction-Behavior Fusion: Contemporary detection systems fuse code analysis and transaction flow anomaly detection. For example, RPHunter constructs both a SRCG and a Token Flow Behavior Graph (TFBG) and leverages graph neural networks to jointly embed and classify tokens, reaching 94.5% F1 in ground-truth datasets (Wu et al., 23 Jun 2025).
• Formal Model Checking: In Bitcoin Trace-Net, protocol traces—including all on-chain and off-chain actions, time-lock constraints, and adversarial reorganizations—are modeled as an extended Petri Net with an explicit Dolev–Yao actor knowledge model. Reachability analysis guarantees that every possible path from the initial contract state culminates in at least one trustless (i.e., non-rugged) safe terminal state (Chiang, 2020).

$$\mathcal{Z} = (K_0^{int}, K_0^{ext}, P, T, F, I_{older}, I_{after}, m_0, b_0)$$

Combined with symbolic actor knowledge, the reachability graph exposes unsafe termination traces, indicating rug pull potential if no safe pathway exists for an honest participant.

• Risk Scoring and Dataset Coverage: Large-scale analyses (e.g., 49,940 NFT contracts) employ risk scoring frameworks, assigning weighted penalties for the presence of rug pull indicators. Roughly 22.6% of NFT contracts were classified as “high risk” based on the sum of indicators, underscoring the prevalence of contract-coded vulnerabilities (Pathade et al., 9 Jun 2025).

3. Financial, Structural, and Lifecycle Implications

The embezzlement pathways enabled by contract-level vulnerabilities translate to quantifiable financial losses and systemic risks:

• Direct Expropriation: Malicious minting or transfer means tokens can be diluted or seized, erasing user funds instantaneously—documented cases report multi-million dollar losses for single events (Lin et al., 3 Mar 2024).
• Liquidity Trap: Limiting sell or burn mechanisms and fake LP locks prevent users from redeeming tokens or withdrawing assets, rendering them valueless (Lin et al., 3 Mar 2024, Sun et al., 24 Mar 2024).
• Recursive and Serial Abuses: Address clustering analyses reveal that rug pull attackers often operate multiple contracts using nearly identical code (AST-Jaccard similarity >0.7 within clusters (Huynh et al., 14 Dec 2024)), enabling serial attacks and highly scalable fraud.
• Contract and Transaction Co-Evolution: In practice, the most sophisticated rug pulls blend code-level exploits with coordinated transaction behavior (pump, dump, and rapid liquidity drain), requiring detection frameworks to model both facets (Wu et al., 23 Jun 2025).

Table: Canonical Impact Pathways

Attack Surface	Immediate Impact	Long-term Effect
Hidden mint/transfer	Token value collapse	Systemic user distrust
Fake LP lock	Liquidity loss	Pool/market illiquidity
Owernship manipulation	Future exploits	Endemic fear of protocol risk

4. Detection Tools, Precision, and Limitations

The most advanced detection systems, based on recent empirical evidence, leverage hybrid workflows:

• CRPWarner: Achieved 91.8% precision, 85.9% recall, and an 88.7% F1 score in identifying malicious contract functions on real-world contract data (Lin et al., 3 Mar 2024).
• RPHunter: Outperformed previous systems by integrating graph neural networks over both SRCG and TFBG graphs, reaching 94.5% F1 and demonstrating ~91% precision on real-world mainnet data (Wu et al., 23 Jun 2025).
• Coverage Gaps: Recent systematization papers highlight that even with the aggregation of 14 detection tools, only 25 out of 34 identified root causes can be flagged, leaving vulnerabilities tied to fake LP locks, ownership transfer, and hidden fees difficult to automatically detect (Sun et al., 24 Mar 2024).

Key limitations include:

• Opaque or unverified contracts thwart source-level static analysis (Sun et al., 24 Mar 2024).
• Obfuscation and dynamic code execution (use of delegatecall, proxy contracts) hinder both static and symbolic detection (Pathade et al., 9 Jun 2025).
• Some attacks remain indistinguishable until after financial loss, hence the imperative for proactive, pre-deployment tooling or real-time on-chain monitoring (Wu et al., 23 Jun 2025).

5. Case Studies and Real-World Outcomes

An abundance of reported cases demonstrate how contract-related rug pulls are instantiated and detected in practice:

• Zero-Day Detection: CRPWarner flagged an Indo Token contract containing a hidden mint function undisclosed by any other security platform, validating the criticality of logic-based static workflows (Lin et al., 3 Mar 2024).
• NFT Ecosystem: Approximately 22.6% of ERC-721 contracts on Ethereum were classified as high risk, with observed scam patterns involving repeated rug pulls by the same clusters of addresses, accelerated “cash-out” within days of launch, and interconnected groups moving funds through intricate chain structures (Sharma et al., 2023, Pathade et al., 9 Jun 2025).
• Network Clustering: Serial scammers on Uniswap and Pancakeswap form address clusters, operating repeated “one-day” rug pulls (over 384,000 scammer addresses) and using contract code cloned with >70% AST-Jaccard similarity. Cluster-aware profit formulas correct inflation induced by wash trading, adjusting scam profitability downwards by more than 30% in analyzed cases (Huynh et al., 14 Dec 2024).

6. Formal Guarantees, Countermeasures, and Future Directions

Advanced formal verification and defense architectures are recommended:

• Formal Trustless Execution Guarantees: Trace-Net’s reachability and knowledge model ensure that no sequence of actions (including adversarial blockchain reorganizations and cryptographic subprotocol manipulations) can deny honest participants a safe, non-rug-pulled outcome prior to commit (Chiang, 2020).
• Integrated Defensive Frameworks: Protocol-level defenses, like those embedded in Rugsafe, provide cryptoeconomic insurance, vault-based recovery, and supply regulation mechanisms that help communities reclaim value even after contract-level rug pulls are executed (PHarr et al., 8 Jul 2025).
• Mitigation Strategies: For developers: enforce renounceOwnership, multi-sig for critical paths, and thorough source code audits; for platforms: require source code verification, autonomous static checks, and risk flagging prior to listing (Pathade et al., 9 Jun 2025, Lin et al., 3 Mar 2024).
• Dynamic, Data-Fused Detection: Fused code and transaction analysis, real-time knowledge graph enrichment (such as Kosmosis, integrating blockchain and social/social media signals (Stangl et al., 30 May 2024)) and learning-based fusion models are essential for defense against evolving scam strategies.

In summary, contract-related rug pulls comprise a distinct, code-level attack surface in DeFi and NFT protocols. Their detection hinges on hybrid semantic, static, and transactional analysis, while their mitigation requires both technical and cryptoeconomic innovation. As rug pull typologies broaden and attackers iterate, comprehensive dataset curation, enhanced tool coverage, and rigorous formal modeling will remain the backbone of the research and operational response to these systemic threats.