Finguard Attack Lab
We don't guess bugs.
We actually break it.
AI generates attack paths from contract structure, then runs millions to tens of millions of security simulations — randomized transaction sequences, state combinations, fund flows, and extreme parameters — against a mainnet fork in a hardened sandbox.
Live Sandbox Replay
Watch Monte Carlo + Foundry Break an Invariant
AI-guided sampling runs randomized transaction sequences on a mainnet fork — the moment an invariant like asset conservation breaks, it emits a reproducible minimized PoC.
Invariants
poolAssets ≥ Σ userDepositstotalSupply == Σ balances|twap − spot| ≤ 5%A scripted replay of a real workflow. Project name and address anonymized.
FinGuard-exclusive engine
AI-Guided Monte Carlo Attack
The AI plans a high-value attack search space, then Monte Carlo sampling fires millions of randomized exploit inputs into a hardened sandbox — coverage-tracked and feedback-learned each round, hunting the edge cases hand-written tests never reach.
- 1search-space-planner
AI Search-Space Planning
The AI reads contract structure and prior findings to map high-value attack surfaces, parameter dimensions, and boundary points.
- 2monte-carlo-sampler
Monte Carlo Sampling
Fires randomized inputs via four strategies — uniform, gaussian, boundary-weighted, and combinatorial — across the planned space.
- 3foundry-runner (fork)
Forge-Fork Sandbox Execution
Every sample is executed against a mainnet fork inside a hardened Docker sandbox — real state, real transactions, no guessing.
- 4coverage_map
Coverage Tracking
A per-dimension coverage heatmap records explored and hit bins, so you can see exactly what was tested — not a black box.
- 5feedback-learner
Feedback-Guided Learning
Hits and near-hits feed back to densify sampling around promising regions, chasing edge cases hand-written tests never reach.
- 6hit-confirmation
AI Confirmation + Minimized PoC
Each confirmed break is adjudicated by AI and reduced to a minimal, reproducible proof-of-concept with a fixed seed.
“Millions of attacks” is not a marketing number — every run is auditable and reproducible:
Attack Vector Library
Covering Real-World DeFi Exploits
10 attack templates, 6 of which can cause direct fund loss — each actually executed in the sandbox, not a checklist tick.
Reentrancy — Standard / Cross-Function
Re-enters the target through an external call fired before state is updated, draining balances or corrupting the ledger.
Flash Loan — Aave / Uniswap Path
Amplifies capital with a flash loan to manipulate price or force liquidations within a single block.
Price Manipulation — TWAP / AMM
Distorts oracle prices via large swaps or short TWAP windows to trigger false liquidations or minting.
Access Control — Missing onlyOwner / Proxy
Calls sensitive functions lacking permission guards, or hijacks the implementation via proxy delegatecall.
Frontrunning — Sandwich / Liquidation
Watches the mempool to front-run trades, profiting from sandwich attacks or pre-emptive liquidations.
Integer Overflow — unchecked / Solidity <0.8
Triggers arithmetic overflow inside unchecked blocks or legacy Solidity to bypass balance checks.
Governance — Flash Vote / Timelock Bypass
Borrows voting power in-block to pass malicious proposals, or bypasses the timelock delay.
Rug Pull Detection — Hidden Mint / Blacklist
Detects hidden mint, blacklist freezing, and fake pause traps, then simulates the exit-scam drain.
ERC20 Approval — Unlimited Allowance Drain
Exploits unlimited user approvals to transfer tokens through a malicious or compromised spender.
Cross-Contract — Callback Injection / Fake Token
Deceives target accounting through callback hooks or a forged token address.
Authorized Scope, Fully Isolated
The stronger the offense, the harder the guardrails. Attack Lab only runs within authorized scope — never touching real user assets.
Runs only on contracts / hosts the project owner submitted and authorized; allowlist is fail-closed.
Attack scripts run only against a mainnet fork inside an isolated Docker sandbox — no arbitrary outbound requests.
Public output exposes only scores, severity, and vector class — PoC and bug locations never leak.
Seed, coverage, execution log, and cost ledger are all persisted — reproducible and auditable.
Follows standard disclosure: fix first, publish later. No unauthorized scanning.
Never accesses or downloads real user data — simulation happens on fork snapshots only.
Let the AI Red Team Break It First
Find it before the hackers do. Full 7-layer defense + AI adversarial testing, flat 1599 USDT.