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Defi Attack Patterns

OrganizationPopular
yaklang
defi-attack-patterns

DeFi attack pattern playbook. Use when analyzing flash loan attacks, price oracle manipulation, MEV sandwich attacks, governance exploits, bridge vulnerabilities, and token standard edge cases in decentralized finance protocols.

Overview

Publisheryaklang
Repositoryhack-skills
Skill namedefi-attack-patterns
Stars
2.2K
Forks
292
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by yaklang on GitHub. Read the source before you install it.

Installation

Install the Defi Attack Patterns AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yaklang/hack-skills.git /tmp/hack-skills
mkdir -p .claude/skills
cp -r /tmp/hack-skills/skills/defi-attack-patterns .claude/skills/defi-attack-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Defi Attack Patterns in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Defi Attack Patterns on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Defi Attack Patterns is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

SKILL: DeFi Attack Patterns — Expert Attack Playbook

AI LOAD INSTRUCTION: Expert DeFi exploitation techniques. Covers flash loan mechanics, oracle manipulation (spot vs TWAP), MEV extraction (sandwich, JIT, liquidation), precision loss attacks, governance exploits, bridge vulnerabilities, and token standard pitfalls. Base models often miss the single-transaction atomicity constraint of flash loans and the distinction between spot price and TWAP manipulation.

0. RELATED ROUTING


1. FLASH LOAN ATTACKS

1.1 Mechanism

Flash loans provide uncollateralized borrowing within a single transaction. The entire borrow → use → repay cycle must complete atomically; if repayment fails, the transaction reverts as if nothing happened.

ProviderMax AmountFee
Aave V3Pool liquidity per asset0.05% (can be 0 for approved borrowers)
dYdXPool liquidity0 (uses internal balance manipulation)
Uniswap V3Pool liquidity per pair0.3% (swap fee tier)
BalancerPool liquidityProtocol-configurable

1.2 Price Oracle Manipulation

1. Flash borrow 100,000 WETH
2. Swap 100,000 WETH → TOKEN on AMM_A
   → TOKEN spot price on AMM_A skyrockets
3. On Lending_Protocol (reads AMM_A spot price as oracle):
   → Deposit small TOKEN collateral (valued at inflated price)
   → Borrow large amount of WETH against it
4. Swap TOKEN back → WETH on AMM_A (restore price)
5. Repay flash loan (100,000 WETH + fee)
6. Keep borrowed WETH from Lending_Protocol minus collateral cost

Key insight: protocols using AMM spot reserves (getReserves()) as price oracles are vulnerable. Must use TWAP or external oracle (Chainlink).

1.3 Liquidity Pool Drain via Reentrancy

Flash borrow → deposit into pool → trigger reentrancy during callback → withdraw more than deposited → repay loan.

Exploits the combination of flash loan capital with reentrancy in pool accounting logic.

1.4 Governance Flash Borrow

1. Flash borrow governance tokens
2. Create/vote on malicious proposal (if no snapshot or timelock)
3. Proposal passes instantly
4. Execute proposal (drain treasury, change admin, etc.)
5. Return governance tokens

Defense: snapshot-based voting (Compound Governor Bravo), timelocks, minimum proposal period.


2. PRICE ORACLE MANIPULATION

2.1 Spot Price vs TWAP

Oracle TypeManipulation CostTime Window
Spot price (getReserves())Single large swap (flash loanable)Same transaction
TWAP (Time-Weighted Average)Sustained multi-block manipulationMultiple blocks (expensive)
Chainlink aggregatorCompromise ≥ majority of oracle nodesPractically infeasible

2.2 AMM Manipulation Flow

Normal state: Pool has 1000 ETH + 1,000,000 USDC → price = 1000 USDC/ETH

Attack:
├── Swap 9000 ETH into pool
│   Pool now: 10000 ETH + 100,000 USDC (constant product)
│   Spot price: 10 USDC/ETH (crashed 100x)
├── Dependent contract reads this price
│   → Liquidates positions at wrong price
│   → Or allows cheap borrowing against ETH collateral
├── Swap back: buy ETH with USDC
│   Price restores to ~1000 USDC/ETH
└── Net profit = value extracted from dependent contract - swap slippage - fees

2.3 Chainlink Oracle Staleness

solidity
(, int price, , uint updatedAt, ) = priceFeed.latestRoundData();
// Missing checks:
// 1. price > 0
// 2. updatedAt != 0
// 3. block.timestamp - updatedAt < HEARTBEAT
// 4. answeredInRound >= roundId

If oracle is stale (network congestion, L2 sequencer down), price can be hours old → arbitrage against stale price.

L2 Sequencer Risk: If Arbitrum/Optimism sequencer is down, Chainlink prices freeze. When it comes back, prices jump → mass liquidations at wrong prices.


3. MEV (MAXIMAL EXTRACTABLE VALUE)

3.1 Sandwich Attack

Mempool observation: victim submits swap TOKEN_A → TOKEN_B with slippage 1%

Front-run:  Buy TOKEN_B (increase price)
Victim tx:  Swap executes at worse price (within slippage tolerance)
Back-run:   Sell TOKEN_B (profit from price impact)

Profit = victim's price impact - gas costs × 2

3.2 JIT (Just-In-Time) Liquidity

1. Observe large pending swap in mempool
2. Provide concentrated liquidity in the exact price range (Uniswap V3 tick)
3. Victim's swap executes → JIT LP earns majority of fees
4. Remove liquidity immediately after swap
5. Profit = fee earned - gas - impermanent loss (minimal for single block)

3.3 Liquidation MEV

1. Monitor lending protocols for positions approaching liquidation threshold
2. When price oracle updates → position becomes liquidatable
3. Front-run other liquidators → execute liquidation
4. Receive liquidation bonus (typically 5-15% of collateral)
5. Sell collateral for profit

3.4 MEV Protection Mechanisms

MechanismHow It Works
Flashbots ProtectSends tx to private mempool; only block builder sees it
MEV BlockerRPC endpoint that routes through MEV-aware relayers
Cow Protocol (batch auction)Batch matching eliminates ordering advantage
Encrypted mempoolsThreshold encryption; decrypt only at block build time
MEV-ShareUser captures portion of MEV extracted from their tx

4. PRECISION LOSS EXPLOITATION

4.1 Rounding Errors in Token Calculations

Solidity has no floating point. Integer division truncates:

shares = depositAmount * totalShares / totalAssets

If totalAssets is very large relative to depositAmount * totalShares, result rounds to 0 → depositor gets no shares but pool keeps the deposit.

4.2 First Depositor / Vault Inflation Attack

1. Attacker deposits 1 wei → receives 1 share
2. Attacker donates 1,000,000 tokens directly to vault (not via deposit)
3. Vault state: 1,000,001 tokens, 1 share
4. Victim deposits 999,999 tokens:
   shares = 999,999 * 1 / 1,000,001 = 0 (integer truncation)
5. Victim gets 0 shares; attacker owns 100% of vault (now 2,000,000 tokens)
6. Attacker withdraws all

Defenses:

  • Mint dead shares on first deposit (OpenZeppelin ERC4626 offset)
  • Require minimum initial deposit
  • Internal accounting with virtual offset

4.3 Dust Attack via Precision Truncation

Repeated small operations where each truncation loses 1 wei. Accumulate across thousands of operations → material loss.


5. GOVERNANCE ATTACKS

5.1 Flash Loan Governance

Borrow governance tokens → vote → return. Only works if protocol doesn't snapshot balances before voting.

5.2 Timelock Bypass

VectorMethod
Timelock set to 0Admin can execute proposals instantly
emergencyExecute functionBypasses timelock for "emergencies"
Guardian/multisig overrideSingle point of failure
Proposal cancellation by attackerFront-run with cancel if threshold met

5.3 Quorum Manipulation

Protocol requires 10% quorum (10M tokens out of 100M supply)
├── Flash borrow 10M governance tokens
├── Create proposal: set admin = attacker
├── Vote with borrowed tokens → meets quorum
├── If no timelock: execute immediately
└── Return tokens

6. BRIDGE EXPLOITS

6.1 Common Bridge Attack Vectors

VectorExample
Signature verification bypassRonin Bridge ($624M) — compromised 5/9 validators
Message replayReplay deposit proof on multiple chains
Fake deposit proofSubmit proof for non-existent L1 deposit
Validator collusionCompromised majority of bridge validators
Smart contract bugWormhole ($320M) — uninitialized guardian set
Upgradeable proxy exploitAttacker gains upgrade authority → swap implementation

6.2 Cross-Chain Message Verification

Secure pattern:
├── Source chain: emit event with (destination, amount, nonce, chainId)
├── Relayer: submit proof (Merkle proof of event inclusion)
├── Destination chain: verify proof against known source block header
│   ├── Check nonce not replayed
│   ├── Check chainId matches
│   ├── Verify Merkle proof against trusted root
│   └── Mint/release tokens

Vulnerable pattern:
├── Relayer: submit (destination, amount) signed by N-of-M validators
└── If M is small or keys are compromised → forge signatures

7. TOKEN STANDARD EDGE CASES

7.1 ERC-20 Approval Front-Running

1. Alice approves Bob for 100 tokens
2. Alice wants to change approval to 50 tokens
3. Bob sees the approval change tx in mempool
4. Bob front-runs: transferFrom(Alice, Bob, 100) — uses old approval
5. Alice's approval change executes: approval = 50
6. Bob calls transferFrom(Alice, Bob, 50) — uses new approval
7. Bob extracted 150 tokens instead of 50

Defense: approve(0) first, then approve(newAmount). Or use increaseAllowance/decreaseAllowance.

7.2 ERC-777 Reentrancy via Hooks

ERC-777 tokens call tokensReceived() hook on the recipient before completing the transfer → classic reentrancy vector.

transfer(attacker, amount)
├── _beforeTokenTransfer hook
├── Balance update
├── tokensReceived() callback to recipient  ← reentrancy window
│   └── attacker re-enters: transfer, swap, deposit, etc.
└── _afterTokenTransfer hook

7.3 Fee-on-Transfer Tokens

Tokens that deduct a fee on each transfer. Protocol receives less than amount:

solidity
// Vulnerable: assumes received == amount
token.transferFrom(msg.sender, address(this), amount);
deposits[msg.sender] += amount; // overcredits by fee amount

// Fixed: measure actual balance change
uint before = token.balanceOf(address(this));
token.transferFrom(msg.sender, address(this), amount);
uint received = token.balanceOf(address(this)) - before;
deposits[msg.sender] += received;

7.4 Rebasing Tokens

Tokens that automatically adjust balances (e.g., Aave aTokens, stETH). Protocols holding rebasing tokens may have accounting mismatches if they cache balances.


8. NOTABLE DEFI EXPLOITS REFERENCE

ExploitDateLossPrimary Vector
Ronin BridgeMar 2022$624MCompromised validator keys
WormholeFeb 2022$320MSignature verification bug
BeanstalkApr 2022$182MFlash loan governance
Mango MarketsOct 2022$114MOracle manipulation
Euler FinanceMar 2023$197MDonation attack + liquidation logic
Curve (reentrancy)Jul 2023$73MVyper compiler reentrancy bug

9. DECISION TREE

Analyzing a DeFi protocol?
├── Does it use price oracles?
│   ├── Spot price (AMM reserves)? → Flash loan manipulation (Section 1.2)
│   │   └── Can oracle be manipulated in single tx? → HIGH RISK
│   ├── TWAP? → Multi-block manipulation needed → MEDIUM RISK
│   ├── Chainlink? → Check staleness handling (Section 2.3)
│   │   ├── Heartbeat check present? → OK
│   │   └── L2? → Check sequencer uptime oracle
│   └── Multiple oracles with fallback? → Evaluate each
├── Does it accept external tokens?
│   ├── Yes → Check fee-on-transfer handling (Section 7.3)
│   ├── ERC-777 tokens accepted? → Reentrancy via hooks (Section 7.2)
│   └── Rebasing tokens? → Accounting mismatch (Section 7.4)
├── Does it have governance?
│   ├── Yes → Flash loan governance possible? (Section 5.1)
│   │   ├── Snapshot-based voting? → Safer
│   │   └── Live balance voting? → Flash borrow attack
│   ├── Timelock present? → Check for bypass (Section 5.2)
│   └── Quorum threshold vs flash-loanable supply? (Section 5.3)
├── Is it a vault / yield aggregator?
│   ├── Yes → First depositor attack (Section 4.2)
│   │   └── Virtual offset or dead shares? → Mitigated
│   └── Precision loss in share calculation? (Section 4.1)
├── Is it a bridge?
│   ├── Yes → Load bridge vectors (Section 6)
│   │   ├── Validator set size and key management?
│   │   ├── Replay protection (nonce + chainId)?
│   │   └── Upgradeable? → Who holds upgrade key?
│   └── No → Continue
├── User-facing swap functionality?
│   ├── Yes → MEV exposure (Section 3)
│   │   ├── Slippage protection enforced?
│   │   └── Private mempool integration?
│   └── No → Continue
└── Load [smart-contract-vulnerabilities](../smart-contract-vulnerabilities/SKILL.md)
    for underlying Solidity-level bugs

Frequently asked questions

What does the Defi Attack Patterns AI skill do?

DeFi attack pattern playbook. Use when analyzing flash loan attacks, price oracle manipulation, MEV sandwich attacks, governance exploits, bridge vulnerabilities, and token standard edge cases in decentralized finance protocols.

Why use Defi Attack Patterns on TypingMind?

Because you install it once and use it with any model. Defi Attack Patterns is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Defi Attack Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yaklang/hack-skills/tree/main/skills/defi-attack-patterns. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Defi Attack Patterns?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Defi Attack Patterns?

As many as you like. As long as a model supports skills, you can use Defi Attack Patterns with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Defi Attack Patterns AI skill free?

Yes. It is published on GitHub by yaklang under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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