Bug Hunter Analysis of Yield-Farming Protocols
What is Yield Farming?
Yield farming (a.k.a. liquidity mining) is a DeFi strategy in which users lock assets in protocols—lending pools, AMMs, automated vaults—to earn rewards such as interest, fees, or governance tokens. Farmers constantly shift capital in search of the best returns, so smart-contract reliability is critical. Manual reviews are slow, and many legacy static-analysis tools drown teams in false positives.
Bug Hunter is an automated Solidity code-review engine with < 5 % false-positive rate that gives developers precise, low-noise feedback early in the build cycle. The sections below examine its performance on five prominent yield-farming protocols and the vulnerabilities it detects.
Analyzing Yield-Farming Protocols with Bug Hunter
Table 1 compares the ground-truth issues found by professional auditors with those surfaced by Bug Hunter.
Table 1 — Bug Hunter vs. Human Audits (all findings)
| Project | Audit Findings (T / H / M / L) | Bug Hunter Findings (T / H / M / L) | Accuracy |
|---|---|---|---|
| Badger DAO | 21 (1 / 2 / 18) | 14 (1 / 2 / 11) | 66 % |
| Aave | 53 (0 / 9 / 44) | 25 (0 / 4 / 21) | 47 % |
| Convex | 9 (1 / 3 / 5) | 5 (0 / 2 / 3) | 55 % |
| Eigen Layer | 11 (2 / 2 / 7) | 3 (1 / 1 / 1) | 27 % |
| Compound Finance | 20 (1 / 7 / 12) | 6 (0 / 3 / 3) | 30 % |
| Aggregate | 114 (5 / 23 / 86) | 53 (2 / 12 / 39) | 46.5 % |
With an overall 46.5 % hit rate, Bug Hunter removes nearly half the auditor workload on first pass.
Common Core Functions in Yield-Farming Protocols
Typical yield-farming platforms share battle-tested primitives:
- Deposit / Supply –
deposit(),supply()(ormint()in cTokens) - Withdraw / Redeem – burns receipt tokens and returns principal
- Reward Claim – harvests accrued tokens or fees
- Stake / Unstake – variants of deposit/withdraw
- Emergency Withdraw / Pause – circuit-breakers for crisis response
Because these patterns recur, detection accuracy here is crucial.
Table 2 — Bug Hunter on Common Core Functions
| Project | Audit Findings (T / H / M / L) | Bug Hunter Findings (T / H / M / L) | Accuracy |
|---|---|---|---|
| Badger DAO | 16 (0 / 1 / 15) | 10 (0 / 1 / 9) | 62.5 % |
| Aave | 31 (0 / 3 / 28) | 18 (0 / 3 / 15) | 58 % |
| Convex | 5 (1 / 1 / 3) | 3 (0 / 1 / 2) | 60 % |
| Eigen Layer | 4 (1 / 1 / 2) | 3 (1 / 1 / 1) | 75 % |
| Compound Finance | 13 (1 / 5 / 7) | 6 (0 / 3 / 3) | 46 % |
| Aggregate | 69 (3 / 11 / 55) | 40 (1 / 8 / 31) | 58 % |
Bug Hunter’s accuracy climbs to 58 % on these shared components—evidence of strong generalisation.
Protocol-Specific Functions & Unique Features
Each platform layers bespoke mechanics atop the common base, where novel bugs lurk. Use the toggles to explore key modules audited in our study.
Badger DAO — Bitcoin-Focused Yield Aggregator
- Sett Vaults for BTC-backed assets
- Badger Boost multipliers based on BADGER/bBADGER stake
Aave — Decentralised Lending Pool
- Flash Loans (uncollateralised, same-tx repayment)
- Interest-Rate Switching between stable/variable modes
- Liquidation Call for under-collateralised positions
Convex Finance — Yield Booster for Curve
- cvxCRV wrapper: locks user CRV as veCRV, issues cvxCRV
- Aggregated Voting Power to boost Curve LP rewards
Eigen Layer — Ethereum Restaking Protocol
- Slashing of misbehaving operators
- Operator Registration & Delegation of restaked ETH
Compound Finance — Algorithmic Money Market
- Comptroller governing risk parameters & COMP rewards
- Interest-Rate Accrual per-block via cToken contracts
Table 3 — Bug Hunter on Protocol-Specific Functions
| Project | Audit Findings (T / H / M / L) | Bug Hunter Findings (T / H / M / L) | Accuracy |
|---|---|---|---|
| Badger DAO | 5 (1 / 1 / 3) | 4 (1 / 1 / 2) | 80 % |
| Aave | 22 (0 / 6 / 16) | 7 (0 / 1 / 6) | 31 % |
| Convex | 4 (0 / 2 / 2) | 2 (0 / 1 / 1) | 50 % |
| Eigen Layer | 7 (1 / 1 / 5) | 0 (0 / 0 / 0) | 0 % |
| Compound Finance | 7 (0 / 2 / 5) | 0 (0 / 0 / 0) | 0 % |
| Aggregate | 45 (2 / 13 / 30) | 13 (1 / 3 / 9) | 28.9 % |
Novel, protocol-specific logic is where human expertise still shines—Bug Hunter’s average accuracy drops to 28.9 %.
Conclusive Remarks
- 46.5 % overall and 58 % on common functions demonstrate that Bug Hunter removes a huge chunk of repetitive audit work.
- < 5 % false-positive rate keeps developer focus on genuine issues.
- Performance on bespoke modules (as low as 0 % in some cases) shows automated analysis complements but never replaces expert review.
A layered security strategy—Bug Hunter for constant, automated checks plus manual audits and bug-bounty programmes for complex economics—offers the strongest defence for fast-moving DeFi teams.