The YouTube MEV-Bot Scam: Victims Deploy Their Own Trap

How it works

Fraudsters upload dozens of short tutorials every day from new accounts, each claiming to share a “secret MEV bot” that generates daily ETH profits. Many uploads come from slightly more established profiles with small followings, which makes them look legitimate. Accounts appear and disappear quickly; when one is reported, another appears with the same video and nearly identical thumbnail.

The pattern is consistent: the video description links to raw source code hosted on Pastebin or Google Sites. The presenter tells viewers to open the official Remix IDE (remix.ethereum.org), paste the code, compile, deploy, fund the contract with ETH, then call start() to “launch” the bot. Viewers see confirmations on Etherscan, assume it’s working, and expect profits to start accruing.

But there is no bot. There is only a drain.

The core deception is deeply social: victims are persuaded to do everything themselves. They deploy the smart contract from their own wallet and then manually confirm the transaction in their wallet that sends their funds to the scammer’s contract. It is not a simple button in an interface but a full on-chain operation the victim approves.

There is also an important condition scammers highlight in their videos and comments: “the more ETH you send to the contract, the greater the profit.” This pushes victims to deposit larger amounts, believing they are optimizing returns rather than walking into a trap.

This setup is far more insidious than a phishing site or malicious signature prompt because anti-phishing heuristics don’t trigger. There is no shady website, no spoofed dApp, and no suspicious token approval. Everything happens inside a trusted IDE and feels like normal development work initiated by the victim.      

Key clarification: smart contracts do not run autonomously. On any EVM-compatible network, a contract executes only when a transaction calls one of its functions and then immediately stops. The videos’ promise of a “constant, always-on bot” is a narrative trick rather than how EVM-based blockchains actually work.

Why people fall for it

These tutorials succeed because they weaponize trust.

Human trust cues. A person speaks to the camera with eye contact, gestures, and confident pacing. They show their desktop, type in real time, point to Remix and Etherscan, and narrate “profits” as they appear. It’s classic social engineering adapted to developer pedagogy: the guide doesn’t ask you to believe, it just asks you to build.

The illusion of transparency. “Here’s the source code, paste it into Remix and compile it.” That feels safe because “open source” seems auditable. You see the code, so you assume there’s nothing to hide. On-chain transparency becomes social proof: “Look, the contract is verified on Etherscan; here are the transactions.” To the untrained eye this looks like a test; in reality it’s a trick: visibility without understanding.

Bypassing anti-phishing heuristics. Common instincts like checking the URL, avoiding suspicious signature prompts, not clicking unknown links don’t help here. The URL is Remix; Etherscan is legitimate; there are no token approvals to a random dApp. Each step is normal in isolation; the harm appears only when you string them together under the false belief that you’re launching a bot.

Comment manipulation and channel churn. Comments are often amplified. When a channel is reported or demonetized, another appears with the same script, a different face, and the same call to action. This turnover creates a steady stream of “fresh tutorials” while limiting reputational burn-in on any single account.

Borrowed credibility via thumbnails. Thumbnails often use faces of recognized crypto personalities (or convincing look-alikes) to borrow instant authority. That micro-trust formed in 200 milliseconds materially increases clickthroughs and compliance.

Inside the fake arbitrage bot contract

Let’s walk through the process as victims experience it, then map the on-chain reality that follows.      

The step‑by‑step flow the videos teach:

1. Open Remix: the presenter directs viewers to the official IDE: https://remix.ethereum.org/. This is the standard environment developers use for Solidity experiments.
2. Paste source from a raw page: viewers copy code from Pastebin (raw) or Google Sites. Hosting “open code” on a plain-text site reads as transparent and community-first.
3. Compile and deploy: the tutorial shows how to pick a compiler version, fix imports and deploy. The contract appears on the explorer with bytecode only because the source is not verified. There is no transparency, but most beginners overlook this.
4. Fund the contract: viewers are told to send ETH to the deployed address, often with the familiar line: “the more you fund, the more you earn.”
5. Press “start”: viewers are instructed to call a function start() (sometimes withdraw() or stop()), which supposedly “launches the bot” and starts the profit cycle.      
    

What actually happens under the hood

Although the code looks like a trading system, the only logic that matters boils down to two moves:

1. Obfuscate and reconstruct an attacker address, usually by using a “noisy” string, making small character mutations, then parsing ASCII hex.

2. Transfer the contract’s full balance to that reconstructed address when a public function (often start(), sometimes withdraw() or stop()) is called.

   Here’s a simplified sketch (pseudocode — illustrative only; do not deploy)

function exchange() internal pure returns (address) {

// Carrier string: resembles hex but contains filler characters/offsets

string memory s = "QG384C1A318cE21D85F34A8D2748311EA2F91c84f0";

// Deterministic tiny replacements that “clean” the string at specific indices

s = replaceCharAt(s, 0, '0');

s = replaceCharAt(s, 2, '1');

// ... more small mutations ...

// Parse ASCII hex into a 20-byte address

return parseAddressFromAscii(s, /*start=*/2, /*length=*/40);

}

function start() public payable {

// Transfer full contract balance to computed address

payable(exchange()).transfer(address(this).balance);

} 

The goal is to hide the attacker’s 0x… address in plain sight so it’s not obvious to a casual reader. The code looks like a bot, but it does nothing: no DEX calls, no mempool work, no searchers, no bundles. It’s a string puzzle whose “solution” is an attacker's wallet.

A representative Etherscan trail

We can see the flow in a documented Etherscan trail:

• Contract: https://etherscan.io/address/0x8232aa8c7d721ad5191954371a97a69ddcdcc492
• Victim deploys: https://etherscan.io/tx/0xd825925f923e35533b428b7ae28dd78c2cd3c899645d78019e523d96d254749c      
  
• Victim funds: https://etherscan.io/tx/0xa44e23f6573356582288d754a236e61b1e426bb15924baeb885805545493773e      
  

• Victim calls start(); funds drain: https://etherscan.io/tx/0xc8885e5932894c1c4e4a960af852226f522f36ce1df3c9a6e0588e6503838f81

  

Read the sequence left to right: the victim creates the contract, deposits funds, then presses the button that moves the balance out. That’s why we call these “self-inflicted drains”: the victim does the attacker’s work.

Practical warning: the addresses and contracts linked here are live historical artifacts and may still be monitored by adversaries. Do not interact with them using a wallet.

The anatomy of obfuscation

From a reverse-engineering perspective, the “bot” source is mostly theatre, the meaningful part is the address-hiding technique. Across samples we’ve analyzed, three elements recur:

1) Carrier string

Attackers embed a long string that resembles hex but contains noise characters and offsets. Example:

"QG384C1A318cE21D85F34A8D2748311EA2F91c84f0"

This produces two effects:

• Camouflage. Visually the string doesn’t read as an “attacker wallet.”
• Flexibility. Attackers vary filler characters between videos to avoid simple signature matching.

2) Deterministic character swaps

Tiny helper routines (often misnamed with “trading” names like executeTrades) iterate positions and replace characters. To a non-developer this looks like “algorithmic prep work.” In reality it’s a deterministic cleanup that yields a contiguous 40-character ASCII-hex substring from a fixed offset. Example:      

"QG384..."

executeTrades(..., 0, '0') -> "0G384..."

3) ASCII‑to‑hex parsing

The parser reads 40 characters (20 bytes) from a fixed index, converts each ASCII-hex pair to a byte, accumulates the bytes into a uint160, and casts that value to an Ethereum address. The contract never stores a literal 0x… address.

Concrete first steps:      
• '3' and '8' → 0x38 → first byte      
• '4' and 'C' → 0x4C → second byte      
After 20 pairs the result looks like 0x384C1A31... and is interpreted as an address.

The second part resolves the attacker address and transfers the contract ETH to that address.

Encoding and decoding schemes vary: one video may use one method while another uses a different one. Techniques include byte shuffles, string concatenation/permutation, arithmetic operations, and other runtime transformations that reconstruct the final address. 

How scammers exploit blockchain transparency

The on-chain theatre is as staged as the video.

Simulated profits to build confidence. During demos the presenter often funds the contract from a second wallet to make it look like profits are accruing. The audience sees the balance rise on Etherscan, then a “withdraw” to the presenter’s main address interpreted as proof the bot works. Example demo/ROI address:

https://etherscan.io/address/0xd812a0a4fdb0caa830809d3e79b8028171b00de2      

Seeding victims to nudge bigger deposits. We’ve observed small inbound ETH to newly deployed victim contracts — micro-amounts designed to look like the bot found an opportunity. This encourages victims to send more ETH before pressing the drain function:

https://etherscan.io/address/0x3f58a75965bd363bf45605e0a9a2a6435edfcdfd

Monitoring and manual drains. If a victim funds a contract but stalls (for example, they hesitate to press start()) an attacker can call the drain function. Contracts are public and callable by anyone; the attacker’s wallet or an off-chain bot can sweep balances. Example attacker-triggered withdrawals:

https://etherscan.io/address/0x39e27d5c1729b8a79970a3ed2926b460f07d9592

Channel churn and comment farms. Off-chain the campaign relies on rotating YouTube channels, synthetic comments, and curated thumbnails to maintain velocity and evade moderation. On-chain visibility combined with off-chain persuasion increases conversions and perceived legitimacy.

That’s why we call it a hybrid scam: social engineering sets the expectation, technical manipulation completes the theft.

Why this matters for Web3 security

This scam family reveals structural tensions in the Web3 security model:

• Transparency without comprehension. Open source and on-chain data protect only when users can interpret them. In these tutorials, transparency is a prop, not a control.
• Developer workflows as an attack surface. Remix is an excellent tool, but it lowers barriers so much that copying code from raw pages feels routine. Attackers hijack developer muscle memory: paste, compile, deploy, fund, call. Each step feels like building.
• Developer workflows as an attack surface. Remix is an excellent tool, but it lowers barriers so much that copying code from raw pages feels routine. Attackers hijack developer muscle memory: paste, compile, deploy, fund, call. Each step feels like building.
• Defense tooling gaps. Traditional wallet anti-phishing focuses on suspicious URLs, approval patterns, or signature prompts. These scams evade that layer because there’s no dApp, no token approval, and no domain to blacklist; the transaction looks like a call to your own contract.
• User psychology at scale. YouTube scales persuasion faster than audits can. A human presenter creates parasocial trust; the Remix/Etherscan workflow creates technical trust; cherry-picked on-chain events create empirical trust.
• Education‑style attacks. For enterprises, custodians, and protocols, the lesson is clear: these scams are now part of the threat landscape. Security programs must anticipate scams that recruit the victim as the operator.

That’s why Web3 Antivirus focuses on behavior-level detection and transaction-intent analysis, not just URLs or approvals.      

In conclusion

The strength of this scam is not its code but its choreography. Attackers don’t steal keys or forge signatures; they convince users to deploy and fund the malicious contract themselves.

This reveals a growing blind spot in Web3 where education, open tools, and on-chain transparency overlap. The solution isn’t to restrict experimentation but to build guardrails that flag when open code hides private drains, detect fake profit patterns, and help users separate visibility from verification.

Web3 Antivirus pursues this goal by combining static code analysis with behavioral detection and shared intelligence across platforms. The faster these patterns are shared, the harder these scams become to propagate.

In Web3, the most dangerous exploit is the one that makes users compromise themselves. Verify intent before acting and treat every “MEV bot” tutorial as a potential trap, not a shortcut.