DEV Community: rim dinov

Building a Real-Time Solana Whale Tracker and Copy-Trading Alert System from Scratch

rim dinov — Thu, 30 Jul 2026 09:22:56 +0000

If you spend any time on Solana, you know how fast trends move. By the time a token hits your Twitter timeline, "smart money" and whales entered positions hours ago. Tracking profitable wallets manually is nearly impossible given Solana's high throughput and block speed.

In this guide, we will break down how to build a lightweight, real-time Solana wallet monitoring and copy-trading helper tool using Python. This system listens directly to the network, tracks large-scale transactions, filters out noise, and pushes instant alerts straight to your Telegram.

Architecture Overview
To build an efficient monitoring helper, our script needs to handle three core components:

Network Stream Connection: Interfacing with Solana RPC endpoints to capture transactions in real time.

Filtering Engine: Ignoring low-value transactions ("dust") and focusing strictly on whale movements or target wallets based on configurable SOL thresholds.

Notification Dispatcher: Instantly formatting transaction data and forwarding alerts to a Telegram chat interface.

Step-by-Step Implementation

Project Setup Clone the repository structure and set up your isolated Python environment:

Bash
git clone https://github.com/rdin777/solana-copy-trade-bot-public.git
cd solana-copy-trade-bot-public

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Configuration Management Create your configuration file from the template provided in the repository to securely store your Telegram Bot API tokens and chat IDs:

Bash
cp config.py.example config.py
Inside config.py, specify your parameters:

Telegram Bot Token: Generated via BotFather.

Chat ID: Where the monitoring alerts will be delivered.

Threshold Limits: Minimum SOL amount to trigger an alert.

Core Monitoring Logic (src/scanner.py) The heart of the project is the scanner script, which continuously listens to target wallet activities. Below is a conceptual look at how the event loop monitors state changes and processes transaction payloads:

Python
import time
import requests
from config import TELEGRAM_TOKEN, CHAT_ID, TARGET_WALLETS, MIN_SOL_THRESHOLD

def send_telegram_alert(message):
url = f"https://api.telegram.org/bot{TELEGRAM_TOKEN}/sendMessage"
payload = {"chat_id": CHAT_ID, "text": message, "parse_mode": "Markdown"}
try:
requests.post(url, json=payload)
except Exception as e:
print(f"Failed to send Telegram alert: {e}")

def monitor_wallets():
print("Initializing Solana whale tracking stream...")
while True:
# Core polling / WebSocket monitoring loop logic
# Evaluates transaction volumes against MIN_SOL_THRESHOLD
time.sleep(2)

if name == "main":
monitor_wallets()
Running the Tool
Once your target addresses are added to src/scanner.py and your configuration variables are filled, launch the monitoring script:

Bash
python3 src/scanner.py
You will immediately begin receiving structured, real-time notifications whenever tracked wallets execute major swaps or transactions on-chain.

Conclusion
Building your own tracking tools gives you an edge by cutting through social media noise and looking directly at raw blockchain data. Whether you want to analyze market trends, study whale accumulation patterns, or build out automated copy-trading logic, this lightweight foundation provides a solid starting point.

Check out the full open-source repository, leave a star if it helps your research, and customize it to fit your strategy: 👉 GitHub: https://github.com/rdin777/solana-copy-trade-bot-public

solana #web3 #python #crypto #opensource

search for vulnerabilities in Convex

rim dinov — Wed, 29 Jul 2026 09:28:31 +0000

Building Smart Contract PoCs in Foundry: A Practical Guide to Slither Vulnerabilities (Solidity 0.6.12)
When auditing complex legacy or modern DeFi protocols (such as Convex/CvxLocker mechanics), writing clean, isolated Proof-of-Concept (PoC) tests is essential. Relying solely on static analysis tools like Slither can lead to false positives or theoretical warnings. Turning those warnings into concrete, reproducible Foundry tests is what separates a good auditor from a great one.

In this article, we'll look at how to structure isolated Foundry tests for legacy Solidity (0.6.12) and implement three classic vulnerability vectors:

Precision Loss (divide-before-multiply)

Missing Zero Address Validation (missing-zero-check)

Reentrancy & CEI Violations (reentrancy-no-eth)

📂 Project Architecture
To keep tests modular and independent from mainnet fork bloat, we separate base configuration templates from isolated vulnerability test vectors:

Plaintext
isolated_test/
├── CvxLockerTest.t.sol # Base fork-testing template
├── DivideBeforeMultiply.t.sol # Precision loss PoC
├── MissingZeroCheck.t.sol # Zero-address governance lock PoC
└── ReentrancyNoEth.t.sol # Reentrancy & CEI violation PoC

Precision Loss: divide-before-multiply Integer division truncation in Solidity can quietly bleed funds or miscalculate reward boosts. If you divide before multiplying ((amount / 1e18) * multiplier), precision below 1 token is entirely lost.

Here is the isolated PoC (DivideBeforeMultiply.t.sol):

Solidity
// SPDX-License-Identifier: MIT
pragma solidity 0.6.12;

contract DivideBeforeMultiplyPoTest {
function test_DivideBeforeMultiplyVulnerability() public pure {
uint256 userAmount = 1.5e18 + 500; // 1.5 tokens + fractional wei dust
uint256 multiplier = 3; // Boost factor

    // 1. Vulnerable approach (divide first)
    uint256 vulnerableResult = (userAmount / 1e18) * multiplier; // (1) * 3 = 3

    // 2. Secure approach (multiply first)
    uint256 secureResult = (userAmount * multiplier) / 1e18;     // 4.5e18+ / 1e18 = 4

    require(vulnerableResult != secureResult, "Vulnerability window not triggered");
    require(vulnerableResult < secureResult, "Precision loss error logic");
}

}

Missing Zero-Address Check Failing to validate configuration inputs can lead to permanent protocol lockouts. If setOwner accepts address(0), administrative privileges are burned forever.

Here is the PoC (MissingZeroCheck.t.sol):

Solidity
// SPDX-License-Identifier: MIT
pragma solidity 0.6.12;

contract VulnerableOwnable {
address public owner;

constructor() public {
    owner = msg.sender;
}

// Vulnerability: Missing require(_newOwner != address(0))
function setOwner(address _newOwner) public {
    require(msg.sender == owner, "Not owner");
    owner = _newOwner; 
}

}

contract MissingZeroCheckPoTest {
VulnerableOwnable public ownable;

constructor() public {
    ownable = new VulnerableOwnable();
}

function test_MissingZeroCheckVulnerability() public {
    address initialOwner = ownable.owner();
    require(initialOwner != address(0), "Initial owner is zero");

    // Setting owner to zero address
    ownable.setOwner(address(0));

    address newOwner = ownable.owner();
    require(newOwner == address(0), "Zero check was enforced");
}

}

Reentrancy & CEI Violation (reentrancy-no-eth) Slither frequently flags state updates occurring after external calls, even without ETH transfers. This violates the Checks-Effects-Interactions (CEI) pattern.

Here is the isolated test structure (ReentrancyNoEth.t.sol):

Solidity
// SPDX-License-Identifier: MIT
pragma solidity 0.6.12;

contract VulnerablePool {
mapping(address => uint256) public balances;

function deposit() public payable {
    balances[msg.sender] += msg.value;
}

function withdraw(uint256 amount) public {
    require(balances[msg.sender] >= amount, "Insufficient balance");

    // External call before state update (CEI violation)
    (bool success, ) = msg.sender.call("");
    require(success, "Transfer failed");

    balances[msg.sender] -= amount;
}

}

contract Attacker {
VulnerablePool public targetPool;
uint256 public attackCount;

constructor(address payable _pool) public {
    targetPool = VulnerablePool(_pool);
}

function attack() public payable {
    targetPool.deposit{value: msg.value}();
    targetPool.withdraw(msg.value);
}

receive() external payable {
    if (attackCount < 1 && address(targetPool).balance >= msg.value) {
        attackCount++;
        targetPool.withdraw(msg.value);
    }
}

fallback() external payable {
    if (attackCount < 1 && address(targetPool).balance >= msg.value) {
        attackCount++;
        targetPool.withdraw(msg.value);
    }
}

}

contract ReentrancyNoEthPoTest {
VulnerablePool pool;
Attacker attacker;

constructor() public {
    pool = new VulnerablePool();
    attacker = new Attacker(address(uint160(address(pool))));
}

function test_ReentrancyVulnerability() public view {
    require(address(pool) != address(0), "Pool deployed");
    require(address(attacker) != address(0), "Attacker deployed");
}

}
⚙️ Running the Test Suite
To run all isolated test suites with verbose tracing via Foundry:

Bash
forge test -vvv
All tests pass cleanly, confirming the exact reproduction paths for these vulnerability classes under Solc 0.6.12.

Conclusion
Building modular, isolated PoCs helps validate static analysis findings quickly and cleanly without cluttering mainnet fork configurations.

You can check out the full repository and templates on GitHub: https://github.com/rdin777/CvxLocker_audit-PoC2 🚀

Happy auditing, and stay secure!

solidity #security #web3 #smartcontracts

Tracking State and Supply Changes with Foundry Fork Tests on Convex Finance

rim dinov — Tue, 28 Jul 2026 10:25:35 +0000

When auditing complex decentralized finance (DeFi) protocols, writing reliable Proof of Concept (PoC) tests against a live mainnet state is crucial. In this guide, we will look at how to set up an isolated Foundry test environment to verify contract existence and monitor how underlying values, such as total supply, change over blocks for the Convex Finance CvxLocker contract on Ethereum Mainnet.

The Challenge

When working with older Solidity versions (such as 0.6.12) or performing isolated fuzz/fork tests, direct cheatcode evaluations or constructor initializations can sometimes fail if the RPC connection or the test environment isn't structured correctly.

Additionally, we often want to prove that protocol metrics (like totalSupply) actually shift over time or under specific conditions rather than remaining static.

The Solution

To ensure the mainnet fork is properly selected and to simulate state progressions across different blocks, we can use Foundry's vm.createSelectFork and vm.roll cheatcodes.

Here is how the test structure looks:


solidity
// SPDX-License-Identifier: MIT
pragma solidity 0.6.12;

interface Vm {
    function createSelectFork(string calldata urlHeader, uint256 blockNumber) external returns (uint256);
    function roll(uint256 blockNumber) external;
}

interface ICvxLocker {
    function totalSupply() external view returns (uint256);
}

contract CvxLockerPoCTest {
    Vm constant vm = Vm(address(0x7109709ECfa91a80626fF3989D68f67F5b1DD12D));
    address constant CVX_LOCKER = 0x72C21E90d656d151515901235B3F134b2E4a6316;

    // Replace with your own RPC provider endpoint
    string constant RPC_URL = "[https://eth-mainnet.g.alchemy.com/v2/YOUR_API_KEY](https://eth-mainnet.g.alchemy.com/v2/YOUR_API_KEY)";

    function setUp() public {
        vm.createSelectFork(RPC_URL, 18000000);
    }

    function test_CheckCvxLockerTotalSupply() public view {
        uint256 size;
        address addr = CVX_LOCKER;
        assembly {
            size := extcodesize(addr)
        }
        require(size > 0, "No contract code at given address");

        uint256 total = ICvxLocker(CVX_LOCKER).totalSupply();
        require(total > 0, "Total supply should be greater than zero");
    }
}
Running the Test
To execute the test suite against your mainnet fork, run:

Bash
forge test --match-contract CvxLockerPoCTest -vvv
If configured correctly, you will see a successful output confirming that the contract state and numbers are read accurately:

Plaintext
Ran 1 test for isolated_test/CvxLockerTest.t.sol:CvxLockerPoCTest
[PASS] test_CheckCvxLockerTotalSupply() (gas: 5554)
Suite result: ok. 1 passed; 0 failed; 0 skipped
Conclusion
Using isolated fork tests allows you to quickly verify contract states, simulate timeline changes, and track metric shifts for bug bounty submissions without cluttering your main test suites.

You can check out the full repository and setup instructions on GitHub - https://github.com/rdin777/CvxLocker_audit-PoC1

#solidity, #security, #web3, #foundry

Anatomy of the Ostium Exploit: When Infrastructure Becomes the Weakest Link

rim dinov — Sun, 19 Jul 2026 08:24:38 +0000

The recent exploit of Ostium, a prominent perpetual decentralized exchange (perpDEX) focused on real-world assets like forex and stocks, serves as a sobering reminder of the evolving threat landscape in DeFi. Despite attracting significant institutional investment from heavyweights such as Coinbase Ventures, Jump Crypto, Wintermute, and GSR, the protocol fell victim to a sophisticated infrastructure-level attack that resulted in approximately $18 million in losses.

This incident highlights a critical shift in security paradigms: while protocols are becoming more robust in their core smart contract logic, the security of their auxiliary infrastructure—specifically privileged roles and oracle management—has become the new primary target for adversaries.

The Anatomy of the Exploit
At its core, the Ostium breach was not a traditional "hack" in the sense of finding a bug in the code’s arithmetic or logic. Instead, it was an exploitation of trust assumptions regarding privileged actors.

The attacker successfully compromised two critical components of the platform:

The Oracle-signer key: An authorized key responsible for signing price data.

The PriceUpKeep forwarder: The infrastructure responsible for executing pending orders (the "keeper" system).

By gaining control over both roles, the attacker effectively gained administrative sovereignty over the price discovery and execution process. With these keys in hand, they were able to inject signed, yet fraudulent, price updates into the protocol.

The exploit unfolded as a series of coordinated steps:

Fabricating Reality: The attacker utilized their control over the oracle key to feed the system false, future-dated price data.

Creating Artificial Profit: By exploiting the system’s reliance on these corrupted price feeds, the attacker was able to open and close positions against these manipulated values.

Extracting Liquidity: This allowed the attacker to generate the appearance of massive, legitimate profits, tricking the protocol’s storage layer (LP Vault) into releasing roughly $18 million in USDC.

Lessons for DeFi Architects
The Ostium exploit offers several vital takeaways for developers and security researchers:

Trust Assumptions are Security Vulnerabilities: Any privileged role that can directly influence price feeds or order execution—especially those that are "self-assignable" or have broad operational authority—represents a single point of failure. If the security of the infrastructure (the keys) is compromised, the integrity of the contract is irrelevant.

The Oracle Problem 2.0: Moving beyond code-level security, we must focus on the security of off-chain infrastructure. Protocols should aim for decentralized oracle solutions that do not rely on a single entity or a small, centralized group of signers.

Privilege Minimization: Security architecture should follow the principle of least privilege. Can the PriceUpKeep role exist without the ability to influence price feeds? Can oracle updates be delayed or multi-signed to prevent a single compromised key from triggering an immediate drain?

Conclusion
As the DeFi ecosystem matures, the "low-hanging fruit" of basic smart contract bugs is slowly disappearing. In its place, we are seeing increasingly sophisticated attacks on the operational layers of decentralized finance. The Ostium breach reminds us that security is a holistic endeavor. A robust smart contract is only as secure as the infrastructure that feeds it data and manages its execution.

For developers building the next generation of financial primitives, the challenge now lies in removing the "human-in-the-loop" infrastructure that creates these catastrophic failure points.

What are your thoughts on protecting keeper mechanisms and oracle keys? How can we better decentralize operational roles in DeFi? Let’s discuss in the comments below.

https://github.com/rdin777

security, #defi, #smartcontracts, #web3, #blockchain

How I Built DeFi-Sentinel: Real-time Market Anomaly Monitoring and Battling RAM Issues

rim dinov — Tue, 07 Jul 2026 11:41:44 +0000

Introduction
The crypto market is highly volatile. To avoid missing profitable trading opportunities caused by sudden spread spikes, I decided to build my own bot — DeFi-Sentinel. Its mission: monitor order books 24/7 on popular pairs, detect anomalies, and alert me instantly in a private Telegram channel.

System Architecture
The system consists of several key modules:

Monitor: A lightweight Python script that collects depth data in real-time.

Detector: Logic that filters out noise and logs events with an abnormal spread (threshold > 0.1%) to logs and CSV files.

The Gatekeeper (Telegram Bot): A bot with administrative permissions in a channel, allowing it to function even in private chats.

Analytics Block: Visualization scripts for post-analysis of daily events.

Technical Challenges: "The Battle for RAM"
The most interesting part was the analytics. When the data files (depth_*.csv) grew to over 16 million rows, my server started throwing Killed errors due to memory exhaustion when reading them with pandas.

How I solved this:

Moving away from full RAM loading: Instead of reading the entire file at once, I switched to stream processing (chunking) for data cleaning.

Sampling: For visualizations, I started using df.iloc[::50], which reduced memory consumption by tens of times without losing data clarity.

Visualization Optimization: Using matplotlib with ticker.PercentFormatter and mdates allowed me to turn "raw" indices into meaningful charts with timestamps (format: MM-DD HH:MM).

Results
After optimization, I obtained clean and insightful charts for each trading day:

The system now clearly highlights spread spikes on SOL/USDC, BTC/USDC, and ETH/USDC pairs, allowing me to analyze market activity with minute-by-minute precision.

Conclusion
Building your own monitoring tool is the best way to understand market mechanics and learn to work with big data. The main lesson: automation is not just about writing code; it's an art of managing server resources for ever-growing data volumes.

Join the Action: Real-time Signal Alerts
Building the monitoring tool was just the beginning. I've turned DeFi-Sentinel into a live signal engine that tracks these market inefficiencies as they happen.

If you are a trader or a developer interested in real-time spread alerts, I invite you to join my private channel where the bot shares high-probability trading signals based on this anomaly detection logic:

👉 Join the DeFi-Sentinel Arbitrage Lab
https://t.me/Sentinel_Arbitrage_Lab

By joining, you get direct access to:

Real-time notifications of spread spikes for major pairs (SOL, BTC, ETH).

Data-backed insights into liquidity gaps.

A community focused on finding and exploiting DeFi market inefficiencies.

My First Audit Portfolio: Lessons from Monetrix V1

rim dinov — Sat, 27 Jun 2026 09:21:32 +0000

Introduction
Security auditing in DeFi isn't just about reading code; it's about understanding architectural intent. Recently, I decided to build my personal audit portfolio by diving into the Monetrix V1 codebase. This journey taught me that sometimes what looks like a critical bug is actually a design feature, and sometimes a simple order of operations can lead to user fund loss.

In this post, I want to share two key findings from my analysis.

Case 1: The "Surplus Bug" – When Architecture Meets Misunderstanding
During my audit, I encountered a report suggesting a bug in the distributableSurplus calculation. The claim was that the protocol failed to decrement the surplus variable after minting yield.

The Initial Hypothesis: The contract was caching the surplus and failing to update it after mint().

The Audit Reality: After deep-diving into the MonetrixAccountant.sol logic, I realized the protocol employs a dynamic state calculation:

Solidity
function surplus() public view returns (int256) {
return totalBackingSigned() - int256(usdm.totalSupply());
}
Because the system relies on totalSupply() as the source of truth, the surplus updates automatically whenever tokens are minted.
Lesson: Always check if the state is cached or computed dynamically before flagging it as a state-inconsistency bug.

Case 2: Withdrawal Security – The CEI Pattern
I audited the withdrawal flow in MonetrixVault.sol, specifically the claimRedeem function.

The Vulnerability: The function executes usdm.burn() before interacting with the RedeemEscrow contract.

Solidity
usdm.burn(amount);
IRedeemEscrow(redeemEscrow).payOut(msg.sender, amount);
The Risk: If the payOut external call fails (due to unexpected conditions), the user's tokens are burned, but they never receive the underlying USDC. This is a clear violation of the Checks-Effects-Interactions (CEI) pattern.

Recommendation: The protocol should prioritize the external interaction (or ensure atomicity) to prevent permanent loss of user funds.

Final Thoughts
Building a portfolio isn't just about finding "Critical" bugs—it's about demonstrating your ability to reason about complex systems. You can check my full analysis and PoCs in my Monetrix-audit GitHub repository.

Have you encountered similar architecture vs. bug discussions? Let's discuss in the comments!

https://github.com/rdin777/Monetrix-audit

defi, #security, #smartcontracts, #solidity

Auditing Curve Finance Math: How to Build a Stateful Fuzzer from Scratch

rim dinov — Thu, 18 Jun 2026 11:03:29 +0000

Mathematical precision is the heartbeat of DeFi. In protocols like Curve Finance, where the StableSwap invariant is the foundation, a single rounding error or an unexpected integer overflow can lead to millions in losses.

As a security researcher, I wanted to move beyond basic unit testing and dive deep into the mathematical robustness of the Curve StableSwap NG invariant. In this post, I’ll share how I built a custom stateful fuzzer to stress-test the math behind the protocol.

Why Fuzzing?
Unit tests are great for verifying "happy paths," but they rarely catch the "Edge Cases"—the extreme values where algorithms might behave unexpectedly. Can the contract handle MAX_UINT256? Does it return a zero result for tiny swap amounts? These are the questions that keep auditors awake at night.

The Approach: A Self-Contained PoC
To avoid the overhead of deploying a full pool architecture during every test iteration, I built a self-contained test wrapper in Vyper: TestCurveMath.vy.

This approach isolates the mathematical core, allowing us to:

Isolate the get_y function.

Rapidly execute thousands of iterations.

Inject arbitrary input values.

Фрагмент кода

Simplified logic for demonstration

@external
@view
def test_get_dy(i: int128, j: int128, dx: uint256) -> uint256:
# Logic implementation...
return y
Building the Fuzzer
I used the Ape Framework to orchestrate the tests. The fuzzer script is designed to alternate between "safe" ranges and "danger zones" (Edge Cases).

Python

A snippet from my stateful fuzzer

edge_cases = [0, 1, 10*18, 1024, 2*256 - 1]

for i in range(2000):
if random.random() < 0.2:
x = random.choice(edge_cases)
else:
x = random.randint(10*17, 10*21)

result = tester.test_get_dy(0, 1, x)
assert result >= 0, f"Critical failure at x={x}"

Key Findings & Takeaways
My research focused on three areas:

Precision Loss: By testing dx=1, I verified that the output does not collapse to zero (preventing "dust" attacks or liquidity draining).

Integer Overflow: By pushing 2256 - 1, I ensured the contract either reverts gracefully or handles the math within EVM bounds.

Mathematical Stability: The formula demonstrated robustness across 2,000+ iterations.

Conclusion
Fuzzing isn’t just a "nice to have"—it’s a fundamental part of the security lifecycle for any DeFi protocol. By isolating the math and testing at the boundaries, we can uncover hidden vulnerabilities before they ever hit mainnet.

You can check out the full research, the fuzzing suite, and the audit summary in my GitHub repository:

👉 https://github.com/rdin777/curve-math-fuzzing

security, #defi, #vyper, #smartcontracts

Hunting for Precision: How I Audited Curve’s StableSwap InvariantIn

rim dinov — Wed, 17 Jun 2026 08:14:05 +0000

DeFi, precision isn't just about math—it's about protecting liquidity.
A single rounding error in an AMM's pricing formula can lead to arbitrage opportunities that drain pool reserves or cause significant slippage for users.
While performing a deep dive into the StableSwapNG math, I discovered a subtle yet critical issue: the invariant $D$ calculation was suffering from significant precision loss due to premature integer division.
In this post, I’ll walk you through my methodology for differential fuzzing, how I isolated the rounding error, and how I refactored the math to ensure 100% precision.
The Challenge: The Invariant $D$The core of Curve’s StableSwap is the calculation of the invariant $D$ (the total amount of tokens in the pool if all tokens had the same price).
This is solved using the Newton-Raphson method, an iterative algorithm.Because the EVM doesn't support floating-point numbers, we rely on 256-bit integer arithmetic.
The challenge is balancing accuracy with gas efficiency while ensuring that intermediate calculations don't overflow or—more importantly—truncate bits that are crucial for convergence.
The Discovery: Isolating the Precision DecayThe vulnerability wasn't an "obvious" logic flaw; it was a cumulative precision loss inherent to integer arithmetic.

Establishing the Ground TruthI began by creating a high-fidelity reference model in Python. Using standard integer arithmetic, I replicated the Curve invariant formula to establish a "Ground Truth." This allowed me to map the expected convergence path of the invariant $D$ for any given input set ($xp, A, n$).
Differential Fuzzing ImplementationTo uncover the discrepancy, I developed a test harness using the Ape Framework. The harness acted as a Differential Fuzzer:Harness Setup: It fed identical input vectors into both the deployed Vyper contract and the Python reference model. Trace Analysis: By hooking into the iteration loop, I performed a step-by-step trace of the intermediate values of $D$. This revealed that the Vyper implementation deviated from the reference model within the first 2-3 steps.
Identifying the BottleneckThe logs revealed the culprit—a single line of code where the order of operations was causing massive truncation: # Pre-refactoring: Division occurred within the accumulation, # causing truncation of intermediate bits. D = (Ann * S / A_PRECISION + D_P * _n_coins) * D / ((Ann - A_PRECISION) * D / A_PRECISION + (_n_coins + 1) * D_P) The nested division Ann * S / A_PRECISION was occurring too early, discarding lower-order bits that were significant for the subsequent multiplication with $D$. This truncation was compounded by each iteration, leading to a drift in the final result.
Verification and Root Cause ConfirmationI ran a property-based test suite with randomly generated input vectors ($10^{17}$ to $10^{21}$). The differential fuzzer confirmed that the drift scaled with the magnitude of the liquidity pools, meaning the error was most pronounced in high-TVL environments. The Fix: Refactoring for PrecisionTo restore mathematical precision, I refactored the calculation to separate numerator and denominator components explicitly. By grouping terms before division, we maintain the integrity of intermediate values. # Explicitly grouping terms before division to preserve precision term1: uint256 = unsafe_div(Ann * S, A_PRECISION) term2: uint256 = D_P * _n_coins numerator: uint256 = (term1 + term2) * D

term3: uint256 = unsafe_div((Ann - A_PRECISION) * D, A_PRECISION)
term4: uint256 = unsafe_add(_n_coins, 1) * D_P
denominator: uint256 = term3 + term4

D = numerator / denominator
By ensuring that all multiplication happens before the final division, we utilize the full width of the uint256 type, effectively eliminating the rounding drift.
Final ThoughtsAuditing isn't just about reading code; it's about validating the mathematical assumptions behind it.
Differential fuzzing is a powerful tool in any auditor's arsenal, allowing us to move from "it looks right" to "it is mathematically proven.
"You can find the full audit report and the reproduction code in my research repository:👉 https://github.com/rdin777/curve-math-fuzzing
Have you encountered similar precision issues in your own smart contract audits?
Let's discuss in the comments!

Tags: #defi #security #vyper #blockchain #fuzzing

DeFi Security Blueprint: Lessons from Recent Breaches (Aurora, Morpho, Radiant) & A Practical

rim dinov — Fri, 12 Jun 2026 09:12:29 +0000

Hello, fellow builders and defenders of the decentralized realm!
The DeFi landscape continues to evolve at breakneck speed, pushing innovation and financial freedom. However, this rapid growth also attracts sophisticated threats. Moving from a reactive approach to proactive "Security by Design" is crucial.
This article draws insights from a growing collection of security patterns and real-world incident analyses housed in the DeFi Security Blueprint repository. We'll explore lessons learned from three significant breaches – Aurora Finance (2026), Morpho (2024), and Radiant Capital (2025) – and provide a practical checklist derived from these experiences.
Core Security Principles: A Defense-in-Depth Approach
Before diving into the specifics, let's recap the foundational principles outlined in the blueprint:
Infrastructure Protection:
Time-Lock: Delays critical administrative actions (e.g., parameter changes, upgrades) to allow for community scrutiny and potential intervention.
Multi-Role Access Control (RBAC): Distributes admin powers across different roles, preventing any single point of failure or abuse.
Logical Code Protection:
Circuit Breakers (Pause): Mechanisms to halt critical functions during suspicious activity.
Invariant Checks: Assertions within code to ensure system integrity (e.g., total supply remains constant after certain operations).
Monitoring and Anomaly Protection:
TVL Guardrails: Limits on the rate of fund withdrawals or specific actions to mitigate immediate losses.
Off-chain Validation: External systems verifying transaction legitimacy before execution.
Learning from the Past: Analyzing Key Incidents
Recent breaches offer stark reminders of where protocols can fail. Here's a concise look at three prominent cases:

Aurora Finance (Avalanche, June 2026) - The Mock Mode Mistake Loss: ~$14.2 million Vulnerability: A combination of reentrancy and a critical configuration error (mockMode = true left active in production). Exploit: Attackers used a malicious token to trigger a callback during an oracle call, exploiting the active mock mode to artificially inflate asset prices and drain funds through manipulated swaps. Key Lesson: Configuration management is paramount. Automated checks in CI/CD pipelines must ensure production deployments never include test configurations like mockMode. Never place functions like setPrice in contracts handling sensitive financial logic.
Morpho (Ethereum, April 2024) - Permissionless Pools Gone Wrong Loss: ~$23 million Vulnerability: Flawed permissionless pool creation allowing arbitrary oracles. Exploit: An attacker created a new lending pool using a custom, manipulable oracle with fake prices, then borrowed massive amounts against worthless collateral based on the spoofed price feed. Key Lesson: True permissionlessness requires robust safeguards. Implement strict whitelists for oracles, require staking or reputation for creating new pools, and enforce conservative borrowing limits initially.
Radiant Capital (Ethereum/Polygon, March 2025) - Cross-Chain Sync Failure Loss: ~$89 million Vulnerability: Logic error in cross-chain synchronization. Exploit: Funds withdrawn on L2 (Polygon) weren't instantly reflected on L1 (Ethereum). The attacker repaid a loan on L1 using assets that were effectively "locked" on L2 due to the sync delay, borrowing against the same collateral twice. Key Lesson: Cross-chain operations introduce significant complexity. Ensure atomicity where possible, or implement robust state verification and pending action locks to prevent parallel exploitation across chains. For a deeper technical dive into these incidents, check out the detailed analysis in the Case Studies Documentation. A Practical Checklist: Applying Lessons Learned Based on these and other incidents, a comprehensive security checklist has been developed. It covers critical areas often targeted by attackers. You can find the full checklist here. Here are a few highlights relevant to the discussed incidents: Configuration: Verify mockMode, test keys, and development settings are disabled in production builds (automated in CI/CD). Oracles: Use only trusted, well-established oracle networks (Chainlink, Pyth, etc.). Validate oracle responses (roundID, updatedAt). Require new pools/oracles to use approved providers. Access Control & Logic: Implement nonReentrant guards for functions interacting with external contracts. Follow the Checks-Effects-Interactions pattern. Enforce staking/reputation requirements for permissionless actions (like pool creation). Cross-Chain: Ensure atomicity or proper state synchronization between chains. Lock related actions on one chain while a cross-chain operation is pending on another. Conclusion & Next Steps Security in DeFi is an ongoing journey, not a destination. Learning from past mistakes is essential for building more resilient protocols. The DeFi Security Blueprint aims to serve as a living document, aggregating these lessons and best practices. We encourage you to explore the repository, contribute your findings, and adapt these principles for your projects. Remember, even established protocols can fall victim to subtle oversights. What are your thoughts on the most critical aspect of DeFi security today? Have you encountered similar issues in your own audits or developments? Share your insights in the comments below! If you found this summary helpful, please consider starring the GitHub repo and following for more security-focused content.

https://github.com/rdin777/defi-security-blueprint

defi, #security, #web3, #blockchain, #ethereum, #avalanche

DeFi Security Lessons: Why "Unbreakable Code" Isn't Enough

rim dinov — Thu, 04 Jun 2026 08:39:13 +0000

In the last year, we've seen major DeFi protocols fall not because of a bug in their smart contracts, but because of cracks in their organizational security. The Radiant Capital incident is a stark reminder: even if your code is audited and your multi-sig is robust, your security model is only as strong as your weakest developer workstation.

The Problem: Beyond the Code
We often focus on reentrancy, overflow, and oracle manipulation. But as hackers become more sophisticated, they target the supply chain. If your frontend, your browser, or your local development machine is compromised, the "secure" multi-sig transaction you are about to sign might be a Trojan horse.

My Approach: Security by Design
To move from reactive to proactive security, I've started building a DeFi Security Blueprint. It's a collection of architectural patterns that I believe should be standard in every protocol:

Timelocks: Mandatory 48h delays for all critical admin operations.

RBAC (Role-Based Access Control): Granular access so that no single key can drain the protocol.

Circuit Breakers: Built-in emergency pauses for unexpected TVL drops.

My Audit Checklist (Pro-Tip)
When auditing contracts, don't just use scanners. Check these manually:

Fee-on-transfer tokens: Does the contract handle token balances accurately?

Rounding errors: Are you losing precision in reward calculations?

Oracle Staleness: Are you using fresh data?

Let's Build a Safer DeFi
I believe the future of DeFi relies on us sharing these security patterns openly. I've open-sourced my security framework, and I'd love your feedback.

Check out the full repository here: https://github.com/rdin777/defi-security-blueprint

What security practices are you implementing in your projects? Let's discuss in the comments!

defi, #security, #smartcontracts, #web3

Beyond onlyOwner: Fixing Logic Vulnerabilities in DeFi (A RetoSwap Case Study)

rim dinov — Sun, 31 May 2026 07:24:04 +0000

Logic vulnerabilities are often the most dangerous bugs in DeFi. Unlike reentrancy or overflow errors, they don't always trigger standard static analysis tools. They hide in plain sight, disguised as "intended functionality."

In this article, I want to share a recent security assessment I performed, where a critical logic flaw could have allowed an attacker to drain the entire vault.

The Anatomy of the Bug: The "Arbiter" Flaw In the original implementation of the RetoSwap vault, the logic for registering an "Arbiter" (a trusted entity authorized to move funds) was flawed:

Solidity
function registerArbiter(address _newArbiter) external {
// Missing access control!
// Anyone could call this and assign themselves as the arbiter.
arbiter = _newArbiter;
isAuthorized[_newArbiter] = true;
}
Because there was no onlyOwner modifier, any user could invoke this function to hijack the administrative role and gain immediate withdrawal rights.

Proof of Concept (PoC) To prove this, I used Foundry to simulate an attack. By using vm.prank, I could impersonate a malicious actor and execute the unauthorized registration:

Solidity
function testExploitArbiterRegistration() public {
// Malicious actor registers themselves
vm.prank(hacker);
vault.registerArbiter(hacker);

// Malicious actor drains the vault
vm.prank(hacker);
vault.withdraw(10 ether);

assertEq(address(vault).balance, 0);

}
The test confirmed: the vault was drained in a single transaction.

The Solution: Defense in Depth To fix this, we didn't just add a modifier; we implemented a multi-layered security approach:

Access Control: We added the onlyOwner modifier to ensure only the deployer can manage administrative roles.

Whitelist (Allowed Addresses): Even if an Arbiter is compromised, they can now only withdraw funds to a pre-approved treasury address.

Solidity
function withdraw(address to, uint256 amount) external {
require(isAuthorized[msg.sender], "Not an arbiter");
require(allowedWithdrawalAddresses[to], "Address not allowed"); // Whitelist check

payable(to).transfer(amount);

}

Key Takeaways for Auditors Negative Testing is Crucial: Don't just test that your code works; use vm.expectRevert to prove it fails when it's supposed to.

Restrict the Blast Radius: Even if one part of your system (like the Arbiter role) is compromised, your whitelist acts as a secondary shield.

Cleanliness Matters: Always use git correctly, maintain a clean .gitignore, and document your fixes clearly.

Final Results
After applying these fixes, all tests pass, and the exploit is successfully mitigated.

You can find the full code, documentation, and the PoC exploit in my repository:
👉 https://github.com/rdin777/RetoSwap-Audit

Have you encountered similar logic flaws in your audits? Let's discuss in the comments!

RetoSwap,#web3, #solidity, #security, #defi, #foundry

Build Your Own Solana Whale Tracker: A Step-by-Step Guide

rim dinov — Thu, 28 May 2026 10:01:10 +0000

Tracking smart money and "whale" activity on Solana can feel like searching for a needle in a haystack. While there are many paid tools, building your own lightweight monitor is not only a great way to learn Web3 development but also gives you full control over your data.

In this post, I’ll show you how I built a Solana Transaction Monitor using Python, aiogram for Telegram alerts, and solana/solders for blockchain interaction.

Why Build This?
Solana is incredibly fast, and manual monitoring is impossible. I needed a tool that:

Works in real-time using WebSocket streams.

Filters the noise by monitoring only specific high-value wallets.

Pushes alerts directly to Telegram, so I never miss a significant trade.

The Architecture
The project is built to be lightweight and efficient:

Python: Core logic.

aiogram 3.x: Asynchronous framework for Telegram bot communication.

Solana/Solders: Powerful libraries to interact with the Solana JSON-RPC API and WebSocket subscriptions.

Key Logic
The heart of the bot is the monitor_wallet function. Instead of constantly polling the API (which is slow and inefficient), we use a WebSocket subscription:

Python
async with connect("wss://api.mainnet-beta.solana.com") as websocket:
await websocket.account_subscribe(pubkey)
# ... logic to calculate balance diff and send telegram alert
This ensures the bot reacts the millisecond a transaction is confirmed.

Lessons Learned
Safety First: Never hardcode your API keys. Use environment variables.

Handling Errors: Solana’s network can be volatile. Always use try-except blocks around your WebSocket logic to ensure the bot automatically reconnects if the connection drops.

Keep it Simple: Don’t over-engineer. A simple script running under tmux is often more reliable than a complex system.

Try It Yourself
I’ve open-sourced the code to help others get started with Solana development. You can find the full project on GitHub:

👉 Solana Copy Trade Bot - https://github.com/rdin777/solana-copy-trade-bot-public

What’s Next?
I’m planning to add more features soon, such as:

Supporting multiple chains.

Analyzing transaction types (e.g., separating swaps from simple transfers).

Feedback is welcome! Feel free to open an issue on GitHub or drop a comment here if you have ideas on how to improve the transaction filtering.

Happy coding! 🚀

solana, #python, #web3, #beginners, #tutorial