Inflated-Seller Wallets in Crypto Markets

• Inflated-seller wallets are specialized blockchain addresses that orchestrate artificial sales cycles to inflate asset prices and obscure illicit extraction in NFT and DeFi markets.
• They employ sophisticated behavioral and algorithmic techniques, including cycle-based graph analysis, to evade traditional on-chain detection frameworks.
• Empirical studies indicate a significant systemic impact, with up to 31.7% of wallets acting as serial scammers and multi-wallet strategies dominating fraudulent schemes.

Inflated-seller wallets are specialized blockchain addresses (wallets) central to a range of digital asset market manipulations. They figure prominently in both NFT wash trading and DeFi rug pulls, executing transactions designed to artificially inflate asset values or to covertly drain pooled liquidity. These wallets adapt sophisticated behavioral and algorithmic techniques to evade traditional detection paradigms, often operating within distributed, multi-actor schemes. The paper of inflated-seller wallets is integral to forensic blockchain analytics, risk scoring in decentralized markets, and the development of defenses against next-generation fraud patterns.

1. Formal Definitions and Prevalence

An inflated-seller wallet is formally defined by its active participation in on-chain cycles or sequences of asset sales that appear economically motivated but are orchestrated for artificial price inflation, sales volume fabrication, or illicit extraction from liquidity pools.

For NFT wash trading (Liu et al., 2023), let $\mathcal{W}$ be the set of all wallets, $\mathcal{T}$ the set of NFT tokens, and let a sale transaction be $\tau = (w_s, w_r, t_j, \theta, p)$. Wallets that repurchase NFT tokens within a short time window ($\Delta t = 30$ days) after selling them—possibly via intermediary addresses—are flagged as wash-sellers, a prototypical type of inflated-seller wallet.

In the DeFi context, within a fragmented rug pull (FRP) (Tran et al., 19 Nov 2025), consider a liquidity pool $\mathcal{P}$, actor set $\mathcal{A}$, and set of sell/withdraw events $T$. Each event $t_i$ by actor $a_i$ with value $v_i$ is evaluated via its reserve impact $Impact(t_i) = v_i / \mathcal{V}_i$. A wallet $a \notin \mathcal{O}$ (non-owner) is inflated-seller if it executes $\geq 1$ sell with $Impact(t_{a,j}) \leq \theta$ (typically $\theta = 0.9$), extracting aggregate value above a profitability threshold. This extends the notion beyond owner-draining addresses.

Empirical data from (Tran et al., 19 Nov 2025) reveals that among 303,614 short-lived LPs, 105,434 (34.7%) were flagged as FRP pools, involving 401,838 inflated-seller wallets and 1,501,408 interacting addresses. NFT marketplaces exhibit similar phenomena, with 0.11% of wallets in popular collections implicated as inflated-sellers (Liu et al., 2023).

2. Evasive Mechanisms and Behavioral Patterns

Inflated-seller wallets exploit fragmentation and actor delegation to evade na\"ive detection schemes.

NFT Wash Trading

Wash trading relies on generating apparent sale events between wallets that are, in reality, controlled by the same actor or colluding group. Cycles—identified on NFT token sale graphs—include intermediary wallets to obscure simple self-dealing. The core evasion strategy is maintaining transaction patterns indistinguishable from legitimate trading when viewed per transaction, only revealing collusion upon holistic, cycle-based graph analysis (Liu et al., 2023).

Fragmented Rug Pulls

FRPs employ three atomic predicates (Tran et al., 19 Nov 2025):

• RetainLP (A): Owner retains control of initial LP tokens, ensuring on-chain liquidity can be continually leveraged for extraction.
• Chop-Thin-Slices (B): Each sell/withdraw transaction is kept below an impact threshold to avoid triggering large-sale alarms.
• Pass-the-Ladle (C): Proceeds are distributed across multiple, often non-owner wallets, complicating tracing and diluting risk.

Table 1 details FRP behavioral archetypes:

Pattern	# Wallets	# Sells
Minimal Drains	1	≤9
Moderate Networks	2–9	≤50
Distributed Campaigns	≥10	50–249
Other Patterns	—	—

Distributed campaigns now dominate, with multi-wallet FRPs reaching ≈70% of cases by 2024.

This suggests continuing adaptation by fraud actors toward more sophisticated, multi-actor, time-extended strategies that evade per-tx and owner-focused rules.

3. Detection Methodologies

Effective detection of inflated-seller wallets requires holistic analysis of transaction graphs, behavioral clustering, and profit attribution. Both domains introduce robust algorithmic frameworks:

NFT Wash Trading Detection (Liu et al., 2023)

• Transaction history graph construction: For each token, directed graphs $G_{t_j}^{\mathrm{sale}}$ and $G_{t_j}^{\mathrm{xfer}}$ are built.
• Cycle detection: Identify $(w_a \to w_b)$ followed by $(w_b \to w_a)$ within $\Delta t$, traversing intermediary edges via breadth-first search.
• Flagging intermediaries: All wallets on detected cycles become suspect as part of coordinated inflation, regardless of direct price manipulation.

FRP Inflated-Seller Wallet Detection (Tran et al., 19 Nov 2025)

• Predicate evaluation: For each LP, scan for RetainLP, then for candidate sellers $a$ who execute one or more sells with $Impact(t_{a,j}) \leq \theta$.
• Profit filter: Only wallets exceeding cumulative minimum extracted value are considered (to filter noise).
• Linkage and time aggregation: Activity is aggregated over the entire LP product life, revealing distributed, slow drains missed by single-event heuristics.

Both approaches undermine legacy rules that assume 1:1 correspondence between scammer and wallet, or rely on large, owner-originated drains.

4. Quantifying Impact and Clusters

A suite of profit and prevalence metrics quantifies inflated-seller wallet impact (Liu et al., 2023, Tran et al., 19 Nov 2025):

• NFT markets: Total price manipulation profit ≈ $930,494, sales profit ≈$1,110,423, repurchase profit ≈ –$1,586,365. Highest-impact wallets make multi-million dollar gains in wash sales.
• DeFi LPs: Top serial inflated-seller wallets participate in up to 16,525 pools; top-5 profits range from $0.5M to$80M (Table 3, (Tran et al., 19 Nov 2025)).
• Temporal trends: Owner participation in inflated selling dropped from ~65% to ~24% (2019–2024), with multi-wallet strategies surging.
• Recurrent actors: 127,252 wallets (31.7% of total) act as serial scammers, appearing in multiple FRPs.

These statistics highlight the scalability and systemic risk posed by inflated-seller wallet rings.

5. Contrast with Legacy Threats and Heuristics

Inflated-seller wallets render traditional, per-transaction or owner-centric detection strategies largely ineffective:

• Legacy rug pull detection: Triggers on large, owner-executed liquidity withdrawals. FRP fragmentation avoids this by slicing and outsourcing sells (Tran et al., 19 Nov 2025).
• NFT wash heuristics: Miss cycles where wallet reuse is obfuscated by intermediaries or time-shifting. Only holistic, history-centric graph traversal reveals full inflation loops (Liu et al., 2023).

A plausible implication is that all state-of-the-art on-chain monitoring must now track cumulative, cross-wallet, and cross-time flows, raising both false positive risks and analytic complexity.

6. Practical Defenses and Open Challenges

Countermeasures and open research problems are prominent (Tran et al., 19 Nov 2025, Liu et al., 2023):

• Early-warning indicators: Burst distributor wallet creation, synchronized small-amount sells, post-exit fund consolidation.
• Automated DEX countermeasures: Adaptive fees or throttling for micro-trade bursts, clustering suspected distributor addresses.
• Algorithmic refinement: Extending analysis windows, integrating cross-wallet linkage (e.g., funding patterns, temporal correlations).
• False positive management: Balancing recall against innocent linkage—especially as scammer behaviors further evolve.
• Continued evasion: Adversaries adapt by leveraging features like time-locked LPs with externalized sell rights or cross-chain bridges to escape detection.

Detection of inflated-seller wallets remains an adversarial race requiring close coupling of algorithmic insight and scalable empirical analysis. Ongoing research aims to unify graph-based, temporal, and economic signals for high-recall, low-bias risk scoring in both NFT and DeFi domains.