Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Week 31 = week ending Sunday, August 2, 2026.

Week 31 saw $31,345,293 extracted from retail investors – up 9.8% from Week 30’s $28,552,789. 7,126 confirmed rug pull events were recorded this week, the highest weekly event count since Week 23. The running total since tracking began now stands at $684,456,935.

Week 31 Key Numbers

Week 31 Analysis

Rug Events Hit Their Highest Level Since Week 23

7,126 rug pull events is the highest weekly count since Week 23’s 8,276, and continues an upward trend that has now run for three consecutive weeks in event count terms even as extracted dollar value dipped in Week 30. New pool mints also jumped 12.8% to 11,000 – the first time mints have crossed 11,000 since Week 27 – suggesting the broader token launch environment picked back up this week after the quieter W29-W30 period.

Fraud Value Reaccelerates, But Roughly in Line With Activity

The 9.8% rise in extracted fraud tracked closely with the 8.5% rise in rug events and the 12.8% rise in new mints – unlike some recent weeks where fraud value and activity moved at very different rates. Average extraction per event was $4,399, up only 1.2% from Week 30’s $4,347, indicating this week’s increase was driven mainly by more rug events happening, not by each individual rug becoming more lucrative.

Removed-to-Added Ratio Eases Slightly

The removed-to-added liquidity ratio was 1.386x this week, down slightly from 1.414x in Week 30 – creators removed $1.39 for every $1.00 they seeded, a marginal easing in extraction efficiency even as raw activity picked up. The rug-to-mint ratio eased to 64.8% from 67.3%, consistent with the larger pool of new mints this week diluting the share that ended in a rug pull, even though the absolute number of rug events rose.

Week-on-Week Comparison: W29 – W31

2026 Running Total – W25 to W31

Note: Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 throughout the tracked period. All figures use the corrected USD-only extraction methodology adopted 2026-08-05.

About This Tracker

ChainAware.ai’s weekly rug pull tracker monitors liquidity removal fraud across PancakeSwap V2 and V3 on BNB Smart Chain and Uniswap V2 and V3 on Ethereum. Rug pull fraud is defined as the USD-equivalent value of liquidity removed by the pool creator that exceeds the USD-equivalent value of liquidity added by the creator – a net extraction event, measured in USD-side value only. The tracker uses ChainAware’s predictive rug pull detection engine, which achieves 90.1% detection accuracy in V3 – see the full Rug Pull Detector methodology for how the model scores deployer wallets and liquidity events.

The post Rug Pull News Week 31 2026 – $31.3M Extracted, Rug Events Hit Highest Level Since Week 23 first appeared on ChainAware.ai.

Data covers ERC-8004 agent registrations and Agent Trust Score assessments. Week 31 = week ending Sunday, August 2, 2026.

Week 31 looked stable at first glance – 21,378 new agents registered, only 7.0% below Week 30’s 22,989. But the number of unique owner wallets behind those registrations collapsed, down 58.7% to just 8,413. The gap between those two numbers tells the real story: registrations concentrated sharply into fewer hands this week. Median trust score also fell further, down 33.1% to 200 – the floor of the Elevated Risk tier, and a value historically associated with farm-flagged registration batches.

Week 31 Key Numbers

Week 31 Analysis

Registrations Held Steady – But Unique Owners Collapsed

Taken on its own, a 7.0% drop in registrations would read as an unremarkable, quiet week. It is the unique owner count that changes the picture entirely: only 8,413 distinct wallets registered agents in Week 31, down from 20,355 in Week 30 – a 58.7% collapse. Registrations fell only slightly while the number of participants fell by more than half, which can only mean one thing: the wallets that did register this week each registered far more agents, on average, than in the prior week.

Agents Per Owner More Than Doubles

Dividing registrations by unique owners gives a rough measure of how concentrated a week’s activity is. In Week 30, that ratio was about 1.13 agents per owner – consistent with most owners registering a single agent, broad-based growth. In Week 31, the same ratio jumped to about 2.54 agents per owner – more than double. This is a marked shift from the pattern described in last week’s tracker, where owner growth closely tracked registration growth. This week, a smaller set of wallets accounts for a similar volume of registrations, which is the signature of batch or fleet-style registration activity rather than broad, independent participation.

Median Trust Hits the Tier Floor

Median trust score fell again, down 33.1% to exactly 200 – the lowest boundary of the Elevated Risk tier, one step above Untrusted. This is not a new value in the tracker’s history: several earlier weeks – W07, W09, W13, W21, and W22 – also recorded a median of exactly 200, and those weeks were consistent with cohorts dominated by low-history or farm-flagged registrations rather than organically diverse agent deployments. Combined with this week’s collapse in unique owners, Week 31 fits that same pattern: a smaller number of wallets registering agents in bulk, with a trust profile that reflects it.

Three Weeks, Three Different Stories

The past three weeks illustrate how differently a similar registration number can behave under the surface. Week 29 saw registrations fall sharply alongside owners, in step with each other. Week 30 saw registrations more than double alongside a nearly equal rise in unique owners – broad-based growth with declining trust. Week 31 saw registrations hold roughly steady while unique owners collapsed – concentrated growth with trust at the tier floor. The registration count alone would not have distinguished any of these three weeks from one another; the unique owner metric is what reveals the difference.

Week-on-Week Comparison: W29 – W31

2026 Weekly Registration History – W25 to W31

Note: Median trust score reflects the median Agent Trust Score of agents registered in that specific week, not the cumulative population median. Unique owner wallets counts distinct wallets that registered at least one agent that week; agents per owner is registrations divided by unique owners for that week.

About This Tracker

ChainAware.ai’s weekly ERC-8004 agent tracker monitors new agent registrations on the ERC-8004 Identity Registry, the number of unique owner wallets behind those registrations, and the median Agent Trust Score of each week’s registration cohort. Agent Trust Score is a 0-1000 score combining owner wallet fraud probability, feeder address analysis, farm detection, and criminal record checks – see the full Agent Trust Score methodology for how the model scores agents and their controlling wallets. Data is updated weekly alongside ChainAware’s PancakeSwap/Uniswap rug pull tracker.

The post ERC-8004 Agent Trust Tracker Week 31 2026 – Unique Owners Collapse 58.7% as Registrations Concentrate and Trust Hits Floor first appeared on ChainAware.ai.

Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Week 30 = week ending Sunday, July 26, 2026.

Week 30 saw $28,552,789 extracted from retail investors across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 – fell 15.4% from the prior week’s $33,740,923. 6,568 confirmed rug pull events were recorded this week. The running total since tracking began now stands at $653,111,642.

Week 30 Key Numbers

Week 30 Analysis

The removed-to-added liquidity ratio stands at 1.414x this week – creators removed $1.41 for every $1.00 they seeded. Average extraction per rug event was $4,347 this week. The rug-to-mint ratio was 67.3% – roughly 7 in every 10 newly minted pools ended in a liquidity extraction event.

Week-on-Week Comparison

Running Total (Corrected)

Note: All figures on this page were corrected on 2026-08-05 to remove a USD double-counting bug in the underlying calculation. Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 throughout the tracked period.

About This Tracker

ChainAware.ai’s weekly rug pull tracker monitors liquidity removal fraud across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Rug pull fraud is defined as the USD-equivalent value of liquidity removed by the pool creator that exceeds the USD-equivalent value of liquidity added by the creator – a net extraction event, measured in USD-side value only. The tracker uses ChainAware’s predictive rug pull detection engine, which achieves 90.1% detection accuracy in V3 – see the full Rug Pull Detector methodology for how the model scores deployer wallets and liquidity events.

The post $28.6M Extracted in Week 30 – Fraud Falls 15.4% WoW [Corrected] first appeared on ChainAware.ai.

Data covers ERC-8004 agent registrations and Agent Trust Score assessments. Week 30 = week ending Sunday, July 26, 2026.

Week 30 saw new ERC-8004 agent registrations more than double. 22,989 new agents registered – up 108.5% from Week 29’s 11,026, the second-highest weekly registration count since tracking began. Unique owner wallets grew at nearly the same pace, up 96.9% to 20,355. But the median trust score of this week’s cohort fell sharply, down 26.4% to 299 – the lowest median trust score recorded since Week 20, and low enough to drop out of the Provisional tier into Elevated Risk.

Week 30 Key Numbers

Week 30 Analysis

Registrations More Than Double

22,989 new agents registered in Week 30, more than double Week 29’s 11,026. This is the second-highest weekly registration count recorded since tracking began, behind only Week 27’s 29,577. Registration volume has now swung sharply for three straight weeks: down 54.9% in Week 29, then up 108.5% in Week 30 – a reminder that weekly registration counts remain highly volatile rather than settling into a stable trend.

Introducing Unique Owners: How Spread Out Is This Week’s Growth?

Starting this week, the tracker also reports unique owner wallets – the number of distinct wallets that registered at least one agent that week. This matters because a large registration number can mean two very different things: it could reflect many different people or teams each registering an agent, or it could reflect a small number of wallets registering large batches of agents at once.

In Week 30, 20,355 unique wallets registered 22,989 agents – an average of about 1.13 agents per owner. In other words, most owners this week registered only a single agent, rather than large batches. Owner wallet growth (+96.9%) closely tracked registration growth (+108.5%), which means the doubling in registrations came from a broad increase in the number of distinct wallets participating, not from a small number of wallets registering many agents each. This is a meaningfully different picture than a scenario where the doubling was driven by one or two operators mass-registering fleets.

Median Trust Score Falls to Its Lowest Since Week 20

Despite registrations being broad-based rather than concentrated in a few wallets, the median trust score of Week 30’s cohort still fell sharply, down 26.4% to 299 – moving out of the Provisional tier (400-599) that the past three weeks had occupied, and into the Elevated Risk tier (200-399). This is the lowest median trust score recorded since Week 20’s 291, and one of the lower readings in the tracker’s history overall. A broad base of new owner wallets does not automatically mean a trustworthy one – many of this week’s registering wallets appear to be new or thin on established on-chain history, which is one of the signals the Agent Trust Score model weighs when assigning a score.

Week-on-Week Comparison: W28 – W30

2026 Weekly Registration History – W24 to W30

Note: Median trust score reflects the median Agent Trust Score of agents registered in that specific week, not the cumulative population median. Unique owner wallets counts distinct wallets that registered at least one agent that week.

About This Tracker

ChainAware.ai’s weekly ERC-8004 agent tracker monitors new agent registrations on the ERC-8004 Identity Registry, the number of unique owner wallets behind those registrations, and the median Agent Trust Score of each week’s registration cohort. Agent Trust Score is a 0-1000 score combining owner wallet fraud probability, feeder address analysis, farm detection, and criminal record checks – see the full Agent Trust Score methodology for how the model scores agents and their controlling wallets. Data is updated weekly alongside ChainAware’s PancakeSwap/Uniswap rug pull tracker.

The post ERC-8004 Agent Trust Tracker Week 30 2026 – Registrations More Than Double to 22,989 as Median Trust Falls to Elevated Risk first appeared on ChainAware.ai.

Source data in this piece comes from “Paper Agents, Paper Gains: An Empirical Analysis of DeFi Investment Agents” (arXiv:2605.29174, May 2026), authored by researchers affiliated with Pantera Capital, Stanford University, IC3, and Ava Labs.

The industry’s own numbers just confirmed the problem Agent Trust Score exists to solve

For the last two years, “AI agent” has been one of the most valuable labels a crypto project could attach to itself. Combined AI-agent token valuations peaked above $3 billion in early 2025, built on a simple pitch: autonomous, on-chain trading systems that manage pooled capital better than a human could.

A new academic study puts real numbers behind that pitch for the first time, and the numbers are not kind. Researchers surveyed over 1,900 AI-tagged crypto projects, narrowed the field to 1,035 that actually function as DeFi investment agents, and then ran a deep architectural analysis of the two dominant frameworks in the space, ElizaOS and Virtuals Protocol, alongside a quantitative performance study of 11 Solana-based agent treasuries covering 925,323 token holders.

The core finding is not that these agents are unprofitable. It is that almost nobody, including the researchers themselves, can actually verify what these agents are doing, which is exactly the gap ChainAware’s Agent Trust Score was built to close.

The scale of the survey is worth sitting with for a moment. The combined dataset pulled from CoinGecko and Messari covered 1,900 projects with recorded token market capitalizations and 182 venture-funded entities totaling $1.87 billion across 229 funding rounds. After applying strict inclusion criteria, filtering out infrastructure plays, data and oracle layers, and traditional robo-advisors that merely touch digital assets, 1,035 projects remained classifiable as genuine DeFi investment agents. From that pool, the researchers selected 10 projects spanning passive, advisory, and active-trading strategies for deep analysis, and found a pattern: projects funded through public token launches consistently showed higher observability, since the token itself creates an on-chain footprint and community incentive for scrutiny, while projects funded through individual user wallets remained largely opaque by default. Observability, in other words, was mostly a side effect of the fundraising mechanism, not a deliberate design choice toward transparency.

Transparency is not the same thing as verifiability

The study’s most important conclusion has nothing to do with returns. It is a methodological wall the researchers hit while trying to do their job: even among the agents with fully public, on-chain wallet addresses, autonomy could not be confirmed.

Of the 10 representative projects examined in depth, only 3 showed clear evidence of autonomous trade execution. Developer interviews with the ElizaOS and Virtuals Protocol teams suggested that a large share of visible deployments amount to basic API integrations rather than genuine autonomous decision-making. And critically, even the 4 highest-observability projects in the study, ones with public wallet addresses that any outsider could audit, could not be independently confirmed as autonomous versus manually operated. A wallet address shows what transaction happened. It does not show who or what decided to make it.

This is the exact distinction ChainAware has been making publicly about agent trust: a score predicts, a record proves. On-chain data alone gives you the record of execution. It does not give you proof of who or what initiated that execution, and without that proof, “autonomous AI agent” is a marketing claim rather than a verifiable fact.

The framework builders say the same thing

The researchers did not have to speculate about this gap. They interviewed core contributors from both ElizaOS and Virtuals Protocol directly, and both teams independently converged on the same conclusion: trust remains the central barrier to adoption, and current-generation LLM agents cannot trade well without human-supplied insight and domain expertise sitting behind them.

Part of why verification is so difficult comes down to how these frameworks are actually built. ElizaOS runs on a plugin architecture: an LLM receives context from data providers, such as wallet balances or price feeds, selects an action from a registry of over 90 available plugins, including token swaps on Solana or spot trading on Hyperliquid, and an evaluator updates memory afterward. The framework itself is open source, but which plugins a specific deployed agent has enabled is not publicly auditable. Virtuals Protocol works differently, mints each agent as an ERC-6551 token-bound account on Base and routes decision-making through a hierarchical planner that delegates to specialized workers, but plugin and strategy selection is similarly developer-controlled and opaque from the outside. In both cases, the framework gives you a wallet address and a record of what it did. It does not give you a way to confirm that a human did not simply sign each transaction manually.

The Virtuals team pointed to founder-side trading expertise, not the underlying framework, as the actual differentiator between agents that perform and agents that fail. That is a notable admission from the team running a platform that has launched over 17,000 agents: the framework itself is not what makes an agent trustworthy or profitable. Something else has to establish that, and today, nothing systematically does, which is the exact gap our suite of AI agent trust and verification tools is built to close.

What that trust gap costs, in dollars

The paper’s performance analysis puts a number on what happens when trust and verification are absent from a market that is otherwise moving billions of dollars.

At their peak, DeFi investment agent token holders across the 11 platforms studied were sitting on more than $2.4 billion in paper gains. By the study’s latest snapshot, that had collapsed into a collective loss of $191.7 million across 925,323 wallets. Only two of the eleven platforms studied still showed positive aggregate user returns.

The distribution of who actually profited is sharply skewed. Across all platforms, the top 1 percent of profitable wallets, just 2,590 out of 259,016 winners, captured 81.4 percent of all gains, or $1.81 billion. The remaining 62.2 percent of participants realized losses. Median returns were negative or effectively zero on every single platform studied, meaning the typical participant in a typical agent token lost money regardless of which platform they chose.

Token price action tells a similar story. Agent tokens declined 93 percent on average from their all-time highs, nearly double the 54 percent peak-to-trough drawdown of a simple SOL buy-and-hold position over the same window. Meanwhile, market-cap-to-AUM ratios for these agent platforms exceeded 10,000x at points, compared to a ratio below 1x for established DeFi protocols like Aave, Curve, and Uniswap. The researchers read this gap as evidence that valuations were being driven by narrative around AI capability rather than by what the treasuries actually held or earned.

The authors are careful to note this is a descriptive finding, not an accusation. They are not arguing these platforms were built to extract value from later entrants. But they do note the resulting payoff structure, early participants capturing outsized gains while the majority of later entrants lose money, resembles the distributional pattern documented in prior research on Ponzi and pyramid schemes in blockchain contexts. The mechanism is different. The shape of who wins and who loses is not.

There is also a timing pattern in the data that reinforces how disconnected these valuations were from underlying agent activity. Market capitalizations across nearly every platform peaked within the same narrow window, late 2024 into early 2025, which points to a shared narrative cycle driving prices rather than individual project fundamentals or trading performance. More tellingly, peak assets under management in agent treasuries tended to arrive after each token’s all-time high, sometimes by weeks, with Virtuals-based agents showing the longest lag. Capital flowed into these treasuries only after the speculative peak had already passed, a reflexive pattern where narrative drove price first and fresh capital followed the narrative rather than the other way around. Treasury and portfolio composition tell a related story: essentially every agent studied held at least one position under $10 million in market cap among its top holdings, a sharp contrast to established DAO treasuries such as Uniswap, Aave, and Arbitrum, whose largest positions all exceed $50 million. Agent treasuries were concentrated in the same small-cap, high-volatility tokens driving the broader speculative cycle, not diversified away from it.

A maturity framework that maps directly onto what Agent Trust Score already does

Rather than stopping at diagnosis, the researchers propose three criteria for what a mature, production-grade DeFi investment agent would need to satisfy:

• Verifiable autonomous execution: observers should be able to determine whether a transaction was initiated by declared agent logic, a human operator, or some other off-chain process, not just that a transaction occurred.
• Sustained risk-adjusted performance: returns benchmarked against passive alternatives across multiple market regimes, not a single favorable cycle.
• Transparent stakeholder alignment: a clear, explicit relationship between what an agent earns, what a treasury retains, and what actually flows back to token holders.

The first criterion is where ChainAware’s Agent Trust Score already operates today. Rather than taking a public wallet address at face value, the score evaluates both the agent wallet and the wallet funding it, the feeder, since a clean-looking deployment wallet funded from a compromised or unknown source is exactly the kind of gap a naive audit would miss. This is a distinct problem from identity verification or social-graph trust, which is why it sits alongside, not in place of, the other layers covered in our five-category breakdown of Web3 trust verification systems. Trust delegation lets an established, well-behaved owner wallet extend a credibility floor to a fresh agent wallet it controls, while a fraud-flagged or unknown feeder drags the score down regardless of how new or old the agent wallet itself is. None of this requires exposing the underlying identity behind either wallet. It requires only that the behavioral signals feeding the score are real, checkable, and resistant to the kind of surface-level gaming a purely public wallet address invites, a distinction we unpack further in our comparison of Web3 reputation scoring approaches.

Put simply: the paper’s Criterion 1 asks the market to stop treating “public wallet address” as a proxy for “verifiable autonomy.” Agent Trust Score was built around that same premise before this paper existed to confirm it, which is precisely why an independent, Pantera-affiliated academic study arriving at the same conclusion matters. It is not ChainAware making the case for itself. It is the market’s own data making it.

Check before you trust an agent, not after

The $191.7 million in aggregate losses documented in this study did not happen because retail participants were reckless. It happened because there was no reliable, independent way to check whether the agent or the wallet behind it deserved trust in the first place, until after the fact, when the paper gains had already turned into real losses.

None of the three maturity criteria the researchers propose are solved by a bigger treasury, a louder narrative, or a longer track record on Crypto Twitter. They are solved by making the thing that actually matters, whether this agent and its owner have earned trust, checkable before capital moves rather than after it disappears.

The post How Should Investors Evaluate AI Trading Agents? A New Study Has Answers first appeared on ChainAware.ai.

Data covers ERC-8004 agent registrations and Agent Trust Score assessments. Week 29 = week ending Sunday, July 19, 2026.

Week 29 brought a sharp pullback in new ERC-8004 agent registrations after two unusually active weeks. 11,026 new agents registered – a 54.9% decline from Week 28’s 24,458 and the lowest weekly registration count since Week 24. The median trust score of this week’s registration cohort also fell, down 12.5% to 406, placing the typical new agent squarely in the Provisional tier. ChainAware now has 317,589 agents indexed across the ERC-8004 Identity Registry.

Week 29 Key Numbers

Week 29 Analysis

A Sharp Reversion After Two Elevated Weeks

Weeks 27 and 28 were the two highest registration weeks since tracking began – 29,577 and 24,458 new agents respectively, well above the roughly 6,000-19,000 range that characterized most weeks from W16 through W26. Week 29’s 11,026 registrations mark a return closer to that longer-run baseline rather than a continuation of the W27-W28 surge. Whether that surge reflected a genuine wave of new agent deployment or a temporary registration campaign by a smaller number of operators is not yet clear from registration counts alone – the trust score data below offers a partial answer.

Median Trust Score Also Declined

Unlike a simple reversion where volume falls but quality holds steady, Week 29’s median trust score of 406 is also down from 464 in Week 28 and 432 in Week 27 – a 12.5% week-on-week decline. All three of the most recent weeks sit in the Provisional tier (400-599), but Week 29 is the weakest of the three. This differs from Week 26, where median trust reached 644 (Trusted tier) alongside a comparatively modest 13,816 registrations – the highest median trust score recorded since tracking began.

The historical range for median weekly trust is wide: from a low of 105 in Week 8 up to 644 in Week 26. Several weeks – W07, W09, W13, W21, and W22 – recorded a median of exactly 200, the floor of the Elevated Risk tier, which is consistent with cohorts dominated by low-history or farm-flagged registrations rather than organically diverse agent deployments.

Most Agents Score Either Very Low or Very High

Weekly registration counts and median scores describe the flow of new agents. A separate, and arguably more important, picture is how trust scores are spread across all 317,589 agents currently indexed. In a healthy, evenly-distributed population, most agents would land somewhere in the middle of the scale. That is not what the data shows. Instead, agents tend to fall into one of two groups: a large cluster with very low scores, and a large cluster with very high scores – with comparatively few agents sitting in between.

On the low end, 27.5% of all indexed agents fall into the Untrusted tier – and within that group, 70,435 agents (22.2% of the entire population) score below 100, the single largest group in the whole dataset. These are typically agents flagged for suspicious patterns: fleets of agents registered together, or agents whose controlling wallet has a history of fraud.

On the high end, 21.3% of agents score in the Sovereign tier – the highest trust level ChainAware assigns – with the very top scoring band (900-1000) alone holding 42,749 agents (13.5%), the second-largest group in the dataset. These are typically agents backed by an owner wallet with a long, clean, well-established on-chain history.

In between these two extremes, the three middle tiers – Elevated Risk, Provisional, and Trusted – are each individually smaller than either the bottom or the top group, even though together they cover a much wider range of scores. In other words: it is more common for an agent to be clearly low-trust or clearly high-trust than to be somewhere in the middle.

This pattern lines up with how ChainAware’s Agent Trust Score is designed to work. Several of the checks behind the score apply sharp penalties rather than gradual ones: agents registered in large batches at the same time are flagged as likely fleets and pushed toward the bottom of the scale, and a confirmed history of fraud in an agent’s ownership chain caps the score outright, regardless of anything else. On the other end, an agent backed by an owner with an established, trustworthy track record is lifted quickly toward the top of the scale. Few agents end up stuck in the middle, because the scoring logic is built to sort agents into clear categories rather than spread them out evenly. For the full scoring methodology behind these tier boundaries, see the Agent Trust Score methodology page.

Week-on-Week Comparison: W27 – W29

2026 Weekly Registration History – W23 to W29

Note: Median trust score reflects the median Agent Trust Score of agents registered in that specific week, not the cumulative population median. Total agents indexed (317,589) reflects the full registry snapshot as of Week 29.

About This Tracker

ChainAware.ai’s weekly ERC-8004 agent tracker monitors new agent registrations on the ERC-8004 Identity Registry and the median Agent Trust Score of each week’s registration cohort. Agent Trust Score is a 0-1000 score combining owner wallet fraud probability, feeder address analysis, farm detection, and criminal record checks – see the full Agent Trust Score methodology for how the model scores agents and their controlling wallets. Data is updated weekly alongside ChainAware’s PancakeSwap/Uniswap rug pull tracker.

The post ERC-8004 Agent Trust Tracker Week 29 2026 – Registrations Fall 54.9% as Most Agents Score Either Very Low or Very High first appeared on ChainAware.ai.

Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Week 29 = week ending Sunday, July 19, 2026.

Week 29 saw $33,740,923 extracted from retail investors across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 – declined 10.0% from the prior week’s $37,486,843. 6,835 confirmed rug pull events were recorded this week. The running total since tracking began now stands at $624,558,853.

Week 29 Key Numbers

Week 29 Analysis

The removed-to-added liquidity ratio stands at 1.519x this week – creators removed $1.52 for every $1.00 they seeded. Average extraction per rug event was $4,936 this week. The rug-to-mint ratio was 64.9% – roughly 6 in every 10 newly minted pools ended in a liquidity extraction event.

Week-on-Week Comparison

Running Total (Corrected)

Note: All figures on this page were corrected on 2026-08-05 to remove a USD double-counting bug in the underlying calculation. Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 throughout the tracked period.

About This Tracker

ChainAware.ai’s weekly rug pull tracker monitors liquidity removal fraud across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Rug pull fraud is defined as the USD-equivalent value of liquidity removed by the pool creator that exceeds the USD-equivalent value of liquidity added by the creator – a net extraction event, measured in USD-side value only. The tracker uses ChainAware’s predictive rug pull detection engine, which achieves 90.1% detection accuracy in V3 – see the full Rug Pull Detector methodology for how the model scores deployer wallets and liquidity events.

The post $33.7M Extracted in Week 29 – Fraud Falls 10.0% WoW [Corrected] first appeared on ChainAware.ai.

Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Week 28 = week ending Sunday, July 12, 2026.

Week 28 saw $37,486,843 extracted from retail investors across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 – surged 16.2% from the prior week’s $32,272,069. 8,343 confirmed rug pull events were recorded this week. The running total since tracking began now stands at $590,817,930.

Week 28 Key Numbers

Week 28 Analysis

The removed-to-added liquidity ratio stands at 1.508x this week – creators removed $1.51 for every $1.00 they seeded. Average extraction per rug event was $4,493 this week. The rug-to-mint ratio was 62.1% – roughly 6 in every 10 newly minted pools ended in a liquidity extraction event.

Week-on-Week Comparison

Running Total (Corrected)

Note: All figures on this page were corrected on 2026-08-05 to remove a USD double-counting bug in the underlying calculation. Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 throughout the tracked period.

About This Tracker

ChainAware.ai’s weekly rug pull tracker monitors liquidity removal fraud across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Rug pull fraud is defined as the USD-equivalent value of liquidity removed by the pool creator that exceeds the USD-equivalent value of liquidity added by the creator – a net extraction event, measured in USD-side value only. The tracker uses ChainAware’s predictive rug pull detection engine, which achieves 90.1% detection accuracy in V3 – see the full Rug Pull Detector methodology for how the model scores deployer wallets and liquidity events.

The post $37.5M Extracted in Week 28 – Fraud Rises 16.2% WoW [Corrected] first appeared on ChainAware.ai.

Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Week 27 = week ending Sunday, July 5, 2026.

Week 27 saw $32,272,069 extracted from retail investors across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 – rose 0.5% from the prior week’s $32,102,733. 8,205 confirmed rug pull events were recorded this week. The running total since tracking began now stands at $553,331,087.

Week 27 Key Numbers

Week 27 Analysis

The removed-to-added liquidity ratio stands at 1.467x this week – creators removed $1.47 for every $1.00 they seeded. Average extraction per rug event was $3,933 this week. The rug-to-mint ratio was 59.0% – roughly 6 in every 10 newly minted pools ended in a liquidity extraction event.

Week-on-Week Comparison

Running Total (Corrected)

Note: All figures on this page were corrected on 2026-08-05 to remove a USD double-counting bug in the underlying calculation. Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 throughout the tracked period.

About This Tracker

ChainAware.ai’s weekly rug pull tracker monitors liquidity removal fraud across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Rug pull fraud is defined as the USD-equivalent value of liquidity removed by the pool creator that exceeds the USD-equivalent value of liquidity added by the creator – a net extraction event, measured in USD-side value only. The tracker uses ChainAware’s predictive rug pull detection engine, which achieves 90.1% detection accuracy in V3 – see the full Rug Pull Detector methodology for how the model scores deployer wallets and liquidity events.

The post $32.3M Extracted in Week 27 – Fraud Rises 0.5% WoW [Corrected] first appeared on ChainAware.ai.

Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Week 26 = week ending Sunday, June 28, 2026.

Week 26 saw $32,102,733 extracted from retail investors across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 – declined 6.1% from the prior week’s $34,193,894. 7,623 confirmed rug pull events were recorded this week. The running total since tracking began now stands at $521,059,018.

Week 26 Key Numbers

Week 26 Analysis

The removed-to-added liquidity ratio stands at 1.485x this week – creators removed $1.49 for every $1.00 they seeded. Average extraction per rug event was $4,211 this week. The rug-to-mint ratio was 60.1% – roughly 6 in every 10 newly minted pools ended in a liquidity extraction event.

Week-on-Week Comparison

Running Total (Corrected)

Note: All figures on this page were corrected on 2026-08-05 to remove a USD double-counting bug in the underlying calculation. Data covers PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3 throughout the tracked period.

About This Tracker

ChainAware.ai’s weekly rug pull tracker monitors liquidity removal fraud across PancakeSwap V2, PancakeSwap V3, Uniswap V2, and Uniswap V3. Rug pull fraud is defined as the USD-equivalent value of liquidity removed by the pool creator that exceeds the USD-equivalent value of liquidity added by the creator – a net extraction event, measured in USD-side value only. The tracker uses ChainAware’s predictive rug pull detection engine, which achieves 90.1% detection accuracy in V3 – see the full Rug Pull Detector methodology for how the model scores deployer wallets and liquidity events.

The post $32.1M Extracted in Week 26 – Fraud Falls 6.1% WoW [Corrected] first appeared on ChainAware.ai.