DEV Community: Duron Epps

The AI Arms Race Hitting Crypto Right Now And Why Your Smart Contracts Are on the Front Line

Duron Epps — Sun, 02 Aug 2026 14:30:00 +0000

By Duron Epps, Founder · August 2026

On May 29, 2026, a security engineer named Taylor Hornby sat down with Anthropic's Claude AI and asked it to audit the cryptographic circuit at the heart of Zcash's private transaction system. Within hours, the AI found what four years of human review had missed: two lines of code that would have allowed any attacker to mint unlimited counterfeit ZEC — silently, invisibly, inside Zcash's shielded pool where no one could detect it.

An emergency hard fork patched the bug by June 1. The public disclosure on June 5 still knocked 40% off Zcash's price overnight.

No funds were confirmed stolen. But here's the uncomfortable reality: because Orchard is a privacy pool, we will never know for certain whether someone had already been exploiting that bug since 2022.

That's the world Web3 builders are operating in now. The same AI capabilities being deployed to find and fix vulnerabilities are being weaponized by the other side. And the gap between "attacked" and "protected" has never been narrower or more dependent on which side moves first.

What Criminals Are Actually Doing With AI Right Now
The $1.5 billion Bybit hack in February 2025 was the largest single cryptocurrency theft in history. Lazarus Group North Korea's state-sponsored hacking unit — didn't find a bug in Bybit's smart contracts. They used a combination of AI-enhanced phishing and social engineering to compromise the Safe multisig infrastructure Bybit relied on for cold wallet management. The AI didn't write the exploit. It wrote the convincing emails, cloned the voices, and identified the specific humans whose authorization was needed.

That distinction matters. The most dangerous AI-powered attacks in crypto right now aren't pure code exploits. They're hybrid attacks that combine two things:

Automated vulnerability discovery. Agentic AI can now scan deployed smart contracts, identify exploitable patterns, and generate working proof-of-concept exploit code faster than any human security researcher. What used to take weeks of manual review takes hours on a $20/month API budget.
Human-layer attacks powered by deepfakes. When the smart contract itself is solid, attackers go after the people with the keys. Deepfake-as-a-Service platforms made voice and video cloning trivially accessible in 2025. AI-generated phishing emails now achieve click-through rates more than four times higher than human-written ones. A single deepfake video call cost UK engineering firm Arup $25.6 million. A voice-cloned WhatsApp message in Hong Kong cost HK$18.5 million. These aren't one-off incidents they're documented playbook tactics now.

The numbers reflect the scale. North Korean state hackers alone stole $2.02 billion in 2025. Total crypto losses that year hit $3.5 billion. In the first half of 2026, Blockaid documented $1.1 billion in losses across 212 verified exploits. AI-driven scam activity rose roughly 500% year over year.

The Zcash Case Is the Most Important Story Nobody Is Framing Correctly
Most coverage of the Zcash disclosure treated it as a scary near-miss. And it was. But the actual lesson is the opposite of what the price drop suggested.

A 4-year-old critical vulnerability, invisible to every human audit that reviewed the Orchard circuit, was found by an AI in a single session. The same technology that criminals are deploying offensively large language model reasoning over complex code was used here to protect an entire ecosystem before a single dollar was stolen.

That's not a coincidence. It's the asymmetry that defines this moment: AI auditing scales in a way human review never can. A human security researcher can deeply review maybe a few thousand lines of complex cryptographic code per day. AI can reason over an entire protocol's codebase in minutes, cross-referencing known vulnerability patterns, checking invariants, and flagging things that shouldn't add up.

The Zcash team moved fast once the bug was found. The question every Web3 team should be asking is: what's sitting in your code right now that four years of human review hasn't caught?

Where Smart Contracts Specifically Get Exploited
The attack surface on a deployed smart contract is different from traditional software. There's no patch server. There's no "roll back the update." When a vulnerability is exploited on-chain, it's usually irreversible. That changes the calculus entirely pre-deployment security isn't a nice-to-have, it's the only window you have.

The vulnerabilities AI catches most reliably are the ones that have historically caused the largest losses:

Reentrancy attacks are still responsible for some of the biggest DeFi exploits despite being a known pattern since the 2016 DAO hack. They're subtle enough that they slip through human review under time pressure. AI doesn't get tired or skip sections.

Access control flaws functions that should be admin-only but aren't, or ownership patterns that can be hijacked are the second most common source of major losses. They often look fine at a glance but fail under specific call sequences.

Oracle manipulation is harder for humans to catch because it requires understanding how the contract interacts with external price feeds in adversarial market conditions. AI can reason about the economic attack vectors, not just the code paths.

Flash loan attack surfaces require thinking about what an attacker can do with effectively unlimited capital for one transaction. Most human reviewers aren't modeling that by default. AI does.

Using AI on the Offensive Side of Your Development Process
The same week Lazarus Group was using AI to social-engineer Bybit's signatories, Shielded Labs was using AI to find a counterfeiting vulnerability in Zcash's cryptographic circuit. The difference in outcome wasn't luck it was who moved first.

The principle applies to every smart contract development team:

Audit before deployment, not after. Every line of code that goes on-chain should be reviewed by AI before it's reviewed by gas fees. The cost difference between catching a reentrancy bug in development and catching it on Ethereum mainnet isn't small it's the difference between a comment in a code review and a $50M exploit post-mortem.

Audit iteratively, not once. Smart contracts change during development. Each meaningful change to ownership logic, fund handling, or external call patterns deserves a fresh review. AI makes that economically viable in a way that paying $50,000 per manual audit never was.

Treat the audit report as investor collateral. A documented AI audit that shows what was checked, what was found, and what was fixed is evidence of professional-grade security practice. DAOs, investors, and institutional partners are increasingly asking for it.

Keep your human layer locked down. AI can audit your code. It can't audit your Telegram DMs or your team's email. The Bybit hack didn't come through a smart contract vulnerability it came through the people with signing authority. Hardware wallets, multisig with time delays, and skepticism toward any urgent authorization request are the non-code layer of defense.

What We're Building
SmartContractAuditor.ai exists because the gap between "teams that can afford a $50,000 manual audit from a top-4 firm" and "teams that deploy with no review at all" was too large, and too dangerous.

Every contract scanned on our platform goes through the same AI reasoning that found the Zcash vulnerability checking reentrancy patterns, access control logic, integer arithmetic, oracle dependencies, flash loan attack surfaces, and 50+ other vulnerability classes. The report you get back isn't a generic checklist. It's specific findings with line-level references, severity scores, exploit scenarios, and fix recommendations written for the vulnerability at hand.

The criminals have the same AI. The question is whether you use it first.

Written by Duron Epps, Founder of SmartContractAuditor.ai · Last updated August 2026

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Sources:

Crypto Hacks Hit All-Time High as North Korea Drains Over $600M and AI Agents Become New Target — TechTimes
AI Phishing, Supply Chains, and $3.5B Lost — Crypto's Brutal 2025 — CryptoNews
The ByBit Heist and the Future of U.S. Crypto Regulation — CSIS
Hackers steal $1.5 billion from exchange Bybit in biggest-ever crypto heist — CNBC
Claude AI Finds Critical Vulnerability in Zcash — Blockhead
Zcash plummets 38% as Shielded Labs reveals a major bug — CoinDesk
AI Deepfakes and Phishing Will Drive Biggest Crypto Hacks in 2026 — CertiK
Deepfake-as-a-Service Exploded in 2025: 2026 Threats Ahead — Cyble

Two Hacks, One Day, Two Completely Different Attack Surfaces

Duron Epps — Fri, 31 Jul 2026 20:35:05 +0000

Today we lost tens of millions of dollars to two separate attacks. Both are worth understanding. They're not the same problem, and they don't have the same solution.

Attack 1: COLDCARD Firmware $38M–$70M in Bitcoin

COLDCARD is one of the most respected Bitcoin hardware wallets on the market. On July 31, 2026, a firmware vulnerability disclosed by Block's security team was actively exploited 594 BTC (approximately $38M at time of disclosure, with totals reported as high as 1,082 BTC/$70M+ as more wallets were drained) disappeared in approximately 25 minutes.

The root cause: a build setting in firmware versions 4.0.0 through 5.0.3 silently disabled the hardware true random number generator. The code that was supposed to check whether the HWRNG was active only verified that the disable setting existed not whether it was actually enabled. With the hardware RNG skipped, seed generation fell back to a software PRNG seeded from the device serial number and internal clock registers both values that are observable and public.

Effective entropy dropped from 128 bits to approximately 40 bits. That makes private keys brute-forceable.

The vulnerable commit is dated March 1, 2021. The flaw went undetected for more than five years. Affected wallets: Mk2 and Mk3 with those firmware versions. If your seed was generated in that window, the seed itself is compromised updating firmware doesn't fix it. You need a new seed and to move funds.

What would have caught this? Not a Solidity audit. Not an EVM security scan. A firmware security audit specifically, NIST SP 800-90A/B/C compliance testing of the RNG implementation. This is a different discipline from smart contract auditing entirely. The code that failed is embedded C in a hardware security module, not a blockchain contract.

Attack 2: CryptoDAO (Pro Token) $8.2M on BNB Chain

On July 29–30, CryptoDAO lost $8.2M USDT from a vault contract on BNB Chain.

The root cause: a vault function with no access control. Any address could call it. The attacker combined flash loans to scale the drain.

This is one of the oldest, most well-documented vulnerability patterns in smart contract security. Missing access control functions that should be restricted to an owner or role but are publicly callable appears in almost every security audit checklist. The SWC registry lists it as SWC-105. Every major Solidity security tool flags it. It has been the root cause of hundreds of millions in losses across the history of DeFi.

What would have caught this? A smart contract audit. An automated scanner. A peer code review. Any one of these would have flagged an unprotected state-changing function on a vault holding $8M in user funds.

The Pattern of 2026

H1 2026 saw $1.1 billion stolen across 212 confirmed incidents the highest first-half figure on record, according to Blockaid's July 2026 report. The distribution is striking:

The two largest incidents (KelpDAO ~$290M, Drift Protocol ~$285M) were attributed to North Korea-linked actors and involved infrastructure compromise key theft, social engineering not smart contract bugs
CEX hacks (CoinDCX $44M, BigONE $28M, WOO X $12M) were driven by employee phishing, malware, and social engineering
DeFi protocol losses included both smart contract bugs (CryptoDAO, Lien Finance, Allbridge) and off-chain key compromise (Ostium $23.75M, AFX Trade $24.15M, B2 Network)
The Rekt News editorial published today is titled "Wrong Attack Surface." The observation: "The primary threat is no longer smart contract code but rather keys, signers, and people around the code." COLDCARD is the clearest single-day illustration of this shift.

What This Means for Developers

The security stack for a crypto project in 2026 has multiple layers, and each layer has its own failure modes:

Layer 1 Smart contract code
Covers: reentrancy, access control, integer overflow, oracle manipulation, flash loan attack surfaces, proxy storage collisions, MEV exposure, denial of service.
How to defend: smart contract audits (manual + automated), fuzzing, formal verification.
Tools: SmartContractAuditor.ai, Slither, Echidna, Certora.

Layer 2 Key management and custody
Covers: hot wallet security, hardware wallet firmware integrity, signer key compromise, multi-sig configuration.
How to defend: hardware security modules, multi-sig with geographic distribution, key ceremony protocols, firmware audits.
The COLDCARD attack lives here.

Layer 3 Off-chain infrastructure
Covers: oracle signer security, bridge validator key management, API endpoint security, server access controls.
How to defend: penetration testing, infrastructure security reviews, monitoring for anomalous signer behavior.
The Ostium ($23.75M) and AFX Trade ($24.15M) attacks from earlier this month live here.

Layer 4 Human and operational
Covers: phishing, social engineering, insider threats, third-party dependencies.
How to defend: security awareness training, separation of privileges, background checks, limited blast radius per role.
CoinDCX ($44M, employee malware) and Drift Protocol ($285M, social engineering) live here.

The Honest Position

A smart contract audit including AI-powered tools like SmartContractAuditor.ai defends Layer 1. It catches what's in the code: the access control failures, the reentrancy vectors, the oracle manipulation surfaces. The CryptoDAO hack ($8.2M) is exactly what these tools exist to prevent.

They don't catch firmware bugs in hardware wallets. They don't catch bridge validator key theft. They don't catch phishing attacks on engineering team members.

That's not a flaw in the category it's an accurate scope boundary. The mistake is treating "we got an audit" as a complete security posture when the attack surface extends well beyond the contract itself.

The COLDCARD flaw sat undetected for five years in hardware that millions of Bitcoin holders trusted. The CryptoDAO vault function sat unprotected for however long it was live, waiting for someone to call it.

Both should have been caught. Neither required a sophisticated attacker. Both were preventable by applying the right defense to the right layer.

Practical Steps for Projects Right Now

Scan your contracts free, takes 60 seconds, catches the CryptoDAO class of vulnerabilities: smartcontractauditor.ai
Check your wallet approvals if you've ever used DeFi, you have open approvals that didn't expire: smartcontractauditor.ai/wallet-approval-checker
Audit your key management who has signing authority for your treasury, bridge validators, oracle signers? Treat key compromise as an active threat model, not an edge case
Update COLDCARD firmware if you hold BTC on Mk2/Mk3, generate a new seed and migrate now. Firmware update alone is not sufficient
Written by Duron Epps, Founder of SmartContractAuditor.ai. Data sourced from CoinDesk, Bitcoin Magazine, CryptoTimes, Blockaid H1 2026 Report, Rekt News. July 31, 2026.

MythX shut down in March and nothing replaced it so I built something

Duron Epps — Thu, 23 Jul 2026 21:52:57 +0000

If you were using MythX before March 31st, you already know.

ConsenSys quietly sunset the entire MythX suite. One day it was there, the next your CI pipeline was just... failing silently. They pointed people toward Diligence Fuzzing, which is a great property-testing tool but it's not an audit tool. Writing fuzz harnesses isn't the same as pasting a contract and getting a vulnerability report back.

I've been building in Web3 security tooling for a while, and I kept running into the same wall: traditional audits cost $10K–$150K and take weeks, open-source scanners like Slither are powerful but raw, and the affordable SaaS middle ground basically disappeared when MythX went dark.

So I spent several months building SmartContractAuditor.ai an AI-powered audit tool that fills that gap.

What it does:

Paste your Solidity, Vyper, or Rust contract and get a categorized report in under 60 seconds
Findings are split into Critical / High / Medium / Low severity
Each finding includes a plain-English explanation of why it's dangerous and how to fix it (not just "SWC-107 detected" with a link to a spec)
Fix verification apply the fix, re-paste, confirm it's gone
PDF report export for sharing with your team or investors
It catches the stuff that causes real losses reentrancy, access control issues, oracle manipulation patterns, flash loan attack surfaces, tx.origin misuse, signature replay, unchecked external calls.

It's free to try 3 scans, no signup required. Just go to the site and paste a contract.

I'm not posting this as a finished, polished product launch. I'm posting because I want feedback from people who actually write smart contracts.

Specifically:

What does the output miss that Slither/Mythril would catch?
Is the severity categorization reasonable, or is it over/under-flagging?
What would make this actually fit into your dev workflow?
Is there a specific vulnerability class you'd want it to prioritize?
If you've been looking for something to replace MythX in your pipeline, give it a shot and tell me what you think either in the comments here or on the site itself.

The tool is at smartcontractauditor.ai no account needed for the first 3 scans.

Polygon and L2 Smart Contract Differences: What Auditors Find That Gets Protocols Rekt

Duron Epps — Tue, 07 Jul 2026 20:51:56 +0000

$3.8M drained from Umbrella Network in February 2022 because the attacker understood how on-chain conditions worked better than the protocol's own team did. The exploit wasn't exotic. It was a price manipulation attack made possible by low liquidity — the exact kind of liquidity environment that's normal on L2s and sidechains at launch. The team built for Ethereum mainnet economics. They deployed somewhere with different rules. That gap cost them everything.

This is the central problem with deploying to Polygon, Arbitrum, Optimism, Base, or any other L2/sidechain: the EVM is largely the same, so developers assume the security model is too. It's not. And auditors who don't understand the specific execution environment they're reviewing will miss the issues that matter most.

Here's What Actually Happens When You Deploy to an L2

The EVM compatibility is real. Your Solidity compiles. Your tests pass. Your mainnet fork works fine. What changes is everything underneath — block timing, gas economics, sequencer behavior, bridging trust assumptions, and oracle reliability. These aren't edge cases. They're load-bearing parts of your security model that work differently depending on where you deploy.

Take block timestamps. On Ethereum mainnet, miners have ~15 seconds of manipulation wiggle room on block timestamps. On Polygon's PoS chain, block times are around 2 seconds and validators control timestamps with similar relative flexibility — but the absolute precision is different, and anything using block.timestamp for time-sensitive logic (vesting, auctions, TWAP windows) needs to account for this. On Arbitrum, block.number doesn't return the Arbitrum block number by default in all contexts — it returns the L1 block number. If you're using block numbers for timing, you might be off by orders of magnitude.

The sequencer is its own attack surface. Arbitrum and Optimism run centralized sequencers right now. A sequencer can front-run your transactions, delay them, or reorder them in ways that Ethereum's mempool can't. This matters for any protocol where transaction ordering creates economic advantage — DEX arbitrage, liquidations, NFT mints with price curves. The sequencer operator has privileges your threat model probably doesn't account for.

And then there's the bridge. Every asset that moves from L1 to L2 went through a bridge contract. Every bridge contract has its own trust assumptions, delay windows, and failure modes. When you write a contract that depends on bridged tokens or cross-chain messages, you're inheriting those assumptions whether you know it or not.

The Code Reality: Where This Breaks in Practice

Here's a pattern I see constantly in L2 contracts — timestamp-dependent logic that worked fine in mainnet testing but creates exploitable windows on faster chains:

Vulnerable Pattern

// Solidity 0.8.24
// DANGEROUS: Assumes block timing similar to Ethereum mainnet
// Deployed on Polygon 2 second blocks destroy this assumption

contract VestingVault {
uint256 public constant VESTING_PERIOD = 30 days;
mapping(address => uint256) public vestingStart;
mapping(address => uint256) public vestedAmount;

function claimVested() external {
    uint256 elapsed = block.timestamp - vestingStart[msg.sender];
    uint256 claimable = (vestedAmount[msg.sender] * elapsed) / VESTING_PERIOD;

    // No upper bound check — on Polygon a validator with timestamp
    // manipulation ability can influence elapsed in a single block
    // In low-liquidity conditions this amplifies with oracle manipulation

    vestedAmount[msg.sender] = 0;
    vestingStart[msg.sender] = block.timestamp;

    token.transfer(msg.sender, claimable);
}

}

The bigger issue here isn't just timestamp manipulation in isolation. It's that on Polygon, if this contract also references an AMM price feed for any reason (say, for USD-denominated limits), the low liquidity typical of Polygon deployments means that price is cheap to manipulate in the same block. Stack the two together and you have a compound exploit vector that looks fine in mainnet fork tests because mainnet has deep liquidity.

The Corrected Version

// Solidity 0.8.24 — L2-aware vesting with OpenZeppelin 5.x patterns
// Works safely on Polygon, Arbitrum, Optimism

import "@openzeppelin/contracts/utils/math/Math.sol";

contract VestingVaultV2 {
uint256 public constant VESTING_PERIOD = 30 days;
uint256 public constant MAX_TIMESTAMP_DRIFT = 15 seconds; // explicit tolerance

struct VestingPosition {
    uint256 totalAmount;
    uint256 claimedAmount;
    uint256 vestingStart;
    uint256 lastClaimTime;
}

mapping(address => VestingPosition) public positions;

function claimVested() external {
    VestingPosition storage pos = positions[msg.sender];
    require(pos.totalAmount > 0, "No position");

    // Cap elapsed at VESTING_PERIOD — no over-claim possible
    uint256 elapsed = Math.min(
        block.timestamp - pos.vestingStart,
        VESTING_PERIOD
    );

    uint256 totalVested = (pos.totalAmount * elapsed) / VESTING_PERIOD;
    uint256 claimable = totalVested - pos.claimedAmount;

    require(claimable > 0, "Nothing to claim");

    // Update state before transfer (reentrancy protection)
    pos.claimedAmount += claimable;
    pos.lastClaimTime = block.timestamp;

    token.transfer(msg.sender, claimable);
}

// Use Chainlink L2 sequencer uptime feed before any price-sensitive logic
// https://docs.chain.link/data-feeds/l2-sequencer-feeds
function _checkSequencerUp() internal view {
    (, int256 answer, uint256 startedAt,,) = sequencerFeed.latestRoundData();
    require(answer == 0, "Sequencer down");
    require(block.timestamp - startedAt > GRACE_PERIOD, "Sequencer restarted recently");
}

}

Notice the _checkSequencerUp() pattern at the bottom. That's something Chainlink provides specifically for L2 deployments. When the Arbitrum or Optimism sequencer goes down and comes back up, price feeds can be stale. Protocols that don't check sequencer status have been exploited during exactly these windows. The Chainlink L2 Sequencer Uptime Feed is a direct fix for this — and it's almost never in contracts I see getting submitted for audit.

The Polygon-Specific Issues Nobody Talks About

Polygon PoS is technically a sidechain, not an L2. The distinction matters for security. Ethereum L2s like Arbitrum and Optimism inherit Ethereum's security for finality — fraud proofs or validity proofs anchor the state to L1. Polygon PoS does not. Its finality is secured by its own validator set, which is ~100 validators with economic security significantly lower than Ethereum mainnet. This is not a knock on Polygon — it's a fact that changes your threat model.

What this means practically:

A 51% attack on Polygon PoS is economically feasible in ways that Ethereum mainnet is not. This happened — Polygon was hit by a critical inflation bug in December 2021 (white-hat disclosure, ~$2M bounty paid) partly because the economic stakes of the chain itself were lower than mainnet

Bridge withdrawals from Polygon to Ethereum take ~3 hours with the PoS bridge and up to 7 days with the Plasma bridge. Contracts that assume atomic cross-chain operations will break

Gas token is MATIC (now POL), not ETH. Any contract that uses msg.value for ETH-denominated pricing and gets deployed to Polygon without adjustment is pricing in the wrong asset

That last one sounds obvious. You'd be surprised. I've seen it.

How I'd Catch This Before It Ships

When I'm auditing a contract destined for an L2 or Polygon, I run a different checklist than mainnet. Here's the actual walk-through:

Grep for every instance of block.timestamp and block.number. For each one, ask: what's the exploit if a validator/sequencer manipulates this by 30 seconds? By 3 minutes? On Polygon with 2-second blocks, 30 seconds is 15 blocks — meaningful for anything with short time windows.
Identify every oracle call. Is it Chainlink? Does it check the sequencer uptime feed? What's the heartbeat and deviation threshold for that specific feed on that specific chain — not mainnet? Chainlink's MATIC/USD feed on Polygon has different parameters than ETH/USD on mainnet. Don't assume.
Map all bridge interactions. Where do tokens come from? If they're bridged, what happens if the bridge is paused? What happens if a message is replayed? The Nomad bridge hack ($190M, August 2022) was a message validation failure — bridged assets are only as safe as the bridge contract's logic.
Check MEV exposure under sequencer conditions. Centralized sequencers on Optimism and Arbitrum can front-run. If your protocol's liquidation mechanism or dutch auction relies on competitive MEV to function correctly, a cooperative sequencer removes that competition. Model what happens when one entity controls ordering.
Test gas assumptions explicitly. L2 gas is cheap and variable in ways mainnet isn't. Contracts that use gas limits as a security mechanism (require(gasleft() > X), or designs that assume certain operations are too expensive to be worth attacking) need re-evaluation. An attack that costs $500 in mainnet gas might cost $0.02 on Polygon.

Static tools like Slither will catch some of the timestamp manipulation patterns and flag missing access controls. They won't catch the economic logic — the "this oracle is manipulable because liquidity is low on this specific chain" issue doesn't show up in a static scan. That requires contextual analysis of the deployment environment.

The Take That'll Surprise You

Everyone auditing L2 contracts is focused on reentrancy and overflow. Those are solved problems in Solidity 0.8.x with OpenZeppelin 5.x. The real killer on L2s right now is deployment environment assumptions — contracts that were secure on mainnet, passed a mainnet audit, and then got deployed to a chain with different block timing, different oracle behavior, and different economic conditions without a re-audit. The contract didn't change. The security model did.

Three protocols I know of have had material losses from exactly this pattern in the last 18 months. None of them made crypto Twitter's highlight reel because the amounts were under $5M. That doesn't mean you want to be the fourth.

Specific Actions to Take Right Now

If you're deploying to Arbitrum or Optimism: Implement the Chainlink L2 Sequencer Uptime Feed check in every function that reads a price oracle. The code is four lines. There's no excuse not to.

Audit your block.number usage: On Arbitrum, block.number returns the L1 block number in some contexts. Use ArbSys(address(100)).arbBlockNumber() if you need the actual Arbitrum block count.

Re-examine every time-locked function with the actual block time of your target chain. A 10-block delay is ~2 minutes on Ethereum. On Polygon it's 20 seconds. That's not a timelock, it's barely a speed bump.

Run your contract on the actual target chain's fork, not mainnet. Use --fork-url pointing to your L2 RPC in Foundry. Mainnet fork tests will lie to you about oracle prices and liquidity depth.

Before you ship: Paste your contract into SmartContractAuditor.ai. It flags L2-specific patterns — sequencer dependency, timestamp manipulation windows, oracle configuration issues — and explains in plain English why each one is dangerous in your deployment context. Thirty seconds before deploy beats thirty days after exploit.

The chains are different. The contracts need to know that. Make sure yours does before someone else discovers it first.

How I Built a Local ONNX AI Detector Inside a Chrome MV3 Extension

Duron Epps — Mon, 29 Jun 2026 21:03:48 +0000

Chrome's Manifest V3 killed a lot of the tricks extension developers relied on. No persistent background pages. No remote code execution. Strict CSP. And if you want to run a machine learning model inside a service worker — have fun, because nothing in the docs tells you how.

Here's how I got ONNX Runtime Web running inside a Chrome MV3 service worker for Faux Spy, an AI image detection extension.

Why Local Inference?

The naive approach: send every image to an API, get a result back. That works, but it's slow (network round trip), costs money for every scan, and fails when the API is down.

Our approach: run a local ONNX model as a pre-filter. If it's confident (>88% AI probability or <12%), return the result immediately. Only send ambiguous cases to the API. This reduces API calls by ~60% and makes obvious cases instant.

The Stack

ONNX Runtime Web (ort.min.js) runs ONNX models in browser JS
SMOGY AI Image Detector a 50MB binary classifier (class 0 = artificial, class 1 = human)
Chrome Cache API caches the model file so it only downloads once
OffscreenCanvas renders images in the service worker without DOM

The Hard Parts

Loading the model in a service worker

Service workers can't use importScripts for WASM files the way regular pages can. We had to import ORT as a module and configure the WASM binary path explicitly:

js
import * as ort from './lib/ort.min.js';
ort.env.wasm.wasmPaths = chrome.runtime.getURL('lib/');

Getting image pixels without canvas access Service workers don't have DOM or canvas access. We use OffscreenCanvas:

const bitmap = await createImageBitmap(blob, { resizeWidth: 224, resizeHeight: 224 });
const canvas = new OffscreenCanvas(224, 224);
const ctx = canvas.getContext('2d');
ctx.drawImage(bitmap, 0, 0);
const imageData = ctx.getImageData(0, 0, 224, 224);

ImageNet normalization in CHW format Most ONNX vision models expect CHW (channels-height-width) format with ImageNet normalization. Converting from RGBA pixel arrays:

const mean = [0.485, 0.456, 0.406];
const std = [0.229, 0.224, 0.225];
const float32 = new Float32Array(3 * 224 * 224);

for (let i = 0; i < 224 * 224; i++) {
float32[i] = (pixels[i*4] / 255 - mean[0]) / std[0]; // R
float32[i + 224*224] = (pixels[i*4+1] / 255 - mean[1]) / std[1]; // G
float32[i + 224*224 * 2] = (pixels[i*4+2] / 255 - mean[2]) / std[2]; // B
}

Label order — a bug that cost us a week The SMOGY model outputs [artificial_logit, human_logit] class 0 is AI, class 1 is real. We had them swapped. Every real image was getting "Definitely Faux" and every AI image was "No AI Detected." Check your label order before shipping.

The Result
Local inference adds ~200ms for the first run (model loading) and ~50ms for subsequent runs. The model achieves ~85% accuracy on easy cases, which is enough to filter before the API handles the hard ones. End-to-end for cached scans: under 100ms.

The extension is on the Chrome Web Store as Faux Spy. The detection architecture is open to questions drop a comment if you're building something similar.

Pre-Deploy vs. Post-Deploy Web3 Security: Two Different Problems

Duron Epps — Mon, 29 Jun 2026 02:39:50 +0000

The $625M Ronin Bridge hack wasn't a code bug it was a social engineering attack on validator keys. The $197M Euler Finance exploit was a code flaw that three separate audit firms missed over seven months in production. Two completely different failure modes, and the Web3 security industry keeps pretending one solution covers both.

It doesn't.

Stage One: Pre-Deployment (The Developer's Problem)

Before a contract is deployed, the threat surface is the code itself. Reentrancy bugs, unchecked return values, access control flaws, oracle manipulation vectors all of these are detectable in Solidity source before any funds are at risk.

The Euler Finance exploit is the clearest example of a pre-deployment failure. The vulnerable donateToReserves() function had been in production since August 2022 seven months before it was exploited in March 2023. Three audit firms reviewed the codebase. None caught it. The bug existed in the code the entire time.

Pre-deploy tools are designed to catch exactly this category of issue:

Reentrancy vulnerabilities the $60M DAO hack pattern, still appearing in 2024
Integer overflow/underflow in Solidity <0.8.0
Unprotected selfdestruct calls
Improper access control on privileged functions
Flash loan attack vectors in AMM logic
tx.origin authentication bypasses

This is where most legitimate audit work happens. A firm like Trail of Bits charges $100k–$300k per engagement to put senior researchers through a codebase for several weeks. AI-assisted tools like SmartContractAuditor.ai run analysis in under 60 seconds — not a replacement for a manual audit on a $50M protocol, but fast enough to surface systematic bugs before you spend $30k on a formal review.

The hard constraint here: none of this is useful after deployment. You can't audit a live contract and un-deploy it. Once the code is on-chain, pre-deploy tooling becomes irrelevant and post-deploy monitoring has to take over.

Stage Two: Post-Deployment (The Trader's Problem)

Once a contract is live, the threat shifts from "is the code broken?" to "is this token a scam?" These are fundamentally different questions that require fundamentally different tools.

Rug pulls don't require buggy code. The Squid Game token rug in October 2021 was technically competent the contract had a sell restriction that prevented anyone but the deployer from exiting. An audit tool would have flagged that function. Most people never looked.

Honeypots are the same pattern: clean-looking tokens where the buy function works perfectly and the sell function silently fails. Over 50,000 honeypot tokens launched on Ethereum in 2023 alone, according to on-chain analysis. A pre-deploy code audit wouldn't help a trader who encountered one of these tokens two weeks after launch — they didn't write the contract, and they never had access to the source.

What post-deploy monitoring catches that static analysis can't:

Rug pulls on newly launched liquidity pools ranked by ETH extracted from victims
Honeypot detection tokens where sells are silently blocked at the contract level
Real-time drain events as they happen, via WebSocket streams
Risk scoring on any deployed token address, in seconds
Trend analytics on scam frequency useful for spotting new attack patterns before they peak

The Nomad Bridge illustrates the cost of missing this layer. It lost $190M in August 2022, four months after a security review. The initial attack exploited a misconfiguration in a single transaction then hundreds of copycat transactions followed automatically. Real-time monitoring would have caught the first anomalous transaction and triggered an alert before most of the $190M drained.

The Workflow That Actually Works

If you're a developer shipping a contract:

Run AI pre-audit on your codebase before touching a formal review — catch the obvious bugs cheaply
Commission a manual audit if your TVL will exceed $1M or you're handling complex DeFi logic
Deploy with anomalous transaction monitoring in place, not just performance monitoring

If you're a trader or investor evaluating a new token:

Check the contract address against a real-time scam database before buying
Verify honeypot status — can you actually sell what you buy?
Set live alerts for drain events on protocols you're exposed to

Why "We Got Audited" Isn't a Security Posture

The audit firm model has dominated Web3 security for years and created a damaging mental shortcut: security = audit checkbox. It doesn't.

Getting audited means a researcher reviewed your code at a single point in time. It says nothing about:

Whether the deployed bytecode matches the reviewed source (it sometimes doesn't)
Whether functions added post-audit introduced new attack surfaces
Whether the token economics create deployer exit incentives
Whether someone is actively draining a liquidity pool right now

Security is a continuous posture, not a one-time event. Pre-deploy tools catch code bugs. Post-deploy monitoring catches live threats. Using only one is like installing a reinforced front door and leaving every window unlocked.

Where to Start

If you're building a contract and want to catch issues before spending on a manual audit: SmartContractAuditor.ai runs in under 60 seconds and is free to start.

If you're trading tokens or need live scam detection infrastructure: RektRadar has a free tier with real-time rug pull tracking and honeypot detection for Ethereum tokens.

Both problems are real. Both ends of the lifecycle need coverage. The teams that treat them as one problem are the ones making headlines for the wrong reasons.

Originally published at https://smartcontractauditor.ai/blog/pre-deploy-vs-post-deploy-web3-security

I Built an AI-Powered Smart Contract Auditor Looking for Feedback from Developers

Duron Epps — Mon, 29 Jun 2026 01:40:17 +0000

Smart contract security has come a long way, but one thing still stands out to me: many vulnerabilities are discovered much later in the development process than they should be.

Professional audits are essential before deploying production contracts, but they're expensive and usually happen near the end of development. I wanted to build something that helps developers catch issues much earlier.

So I started building an AI-powered Smart Contract Auditor.

What It Does

The goal isn't to replace security firms or experienced auditors. Instead, it's designed to act like an always-available security assistant while you're writing code.

Current features include:

Analyze Solidity smart contracts for common vulnerabilities
Detect common security issues such as reentrancy, unchecked external calls, and access control problems
Explain vulnerabilities in plain English
Suggest possible fixes
Generate a security score and audit summary
Produce reports that developers can review before deployment
Why I Started This Project

I've spent a lot of time learning blockchain development and noticed that many developers rely on a combination of documentation, static analyzers, and manual code reviews.

Those tools are incredibly useful, but I wanted something that could also explain why an issue matters instead of simply flagging it.

The goal is to help developers learn while improving their contracts.

Where I'd Like to Take It

Some ideas I'm exploring include:

GitHub repository scanning
Continuous monitoring of contracts
CI/CD integration
VS Code extension
Gas optimization suggestions
Multi-chain support
Interactive AI explanations for vulnerabilities
Security best-practice recommendations
I'd Love Your Feedback

If you're a smart contract developer, security researcher, or auditor, I'd really appreciate your thoughts.

Some questions I have:

What features would make you actually use a tool like this?
What existing tools do you rely on today?
What's your biggest frustration with current smart contract security tooling?
Would AI-assisted vulnerability explanations be useful, or would you rather see traditional static analysis?

Constructive criticism is more valuable than praise at this stage. My goal is to build something developers genuinely find useful.

Thanks for reading, and I look forward to hearing your thoughts.