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Full Audit Checklist — Go MEV Arbitrage Bot

Scope: a production-grade Go service that scans mempool/RPCs, finds arbitrage, constructs & signs transactions, and submits them (e.g., direct RPC, Flashbots bundles). Includes any on-chain smart-contracts the bot depends on (adapters, helpers, custom contracts).

1) Planning & scoping (before running tools)

✅ Identify attack surface: private keys, RPC/WS endpoints, mempool sources, bundle relays (Flashbots), third-party libs, config files.
✅ Define assets at risk (max ETH / tokens), operational windows, and which chains/nodes (e.g., Arbitrum, Ethereum).
✅ Create test accounts with funded testnet funds and a forked mainnet environment (Anvil/Hardhat/Foundry) for reproducible tests.

2) Static analysis — Go (SAST / linters / vuln DB)

Run these to find code smells, insecure patterns, and known vulnerable dependencies:

Tools to run: gosec, govulncheck, staticcheck, golangci-lint. (gosec and govulncheck are high-value for security.) ([GitHub][1])

Example commands:

# gosec
gosec ./...

# govulncheck (requires Go toolchain)
govulncheck ./...

# staticcheck via golangci-lint
golangci-lint run

What to look for:

Use of crypto/rand vs math/rand, insecure parsing of RPC responses, improper TLS skip verify, hard-coded secrets.
Unsafe use of sync primitives, race conditions flagged by go vet/staticcheck patterns.

3) Dynamic analysis & runtime checks — Go

Run go test with race detector and fuzzing (Go 1.18+ built-in fuzz):

# race detector
go test -race ./...

# fuzzing (example fuzz target)
go test -fuzz=Fuzz -fuzztime=10m ./...

Run integration tests on a mainnet-fork (Anvil/Foundry/Hardhat) so you can simulate chain state and mempool behaviours.
Instrument code with pprof and capture CPU/heap profiles during simulated high-throughput runs.

What to look for:

Race detector failures, panics, deadlocks, goroutine leaks (goroutines that grow unbounded during workload).

4) Go security & dependency checks

govulncheck (re: vulnerable dependencies). ([Go Packages][2])
Dependency graph: go list -m all and search for unmaintained or forked packages.
Check for unsafe cgo or native crypto libraries.

5) Code quality & architecture review checklist

Error handling: ensure errors are checked and wrapped (%w) not ignored.
Context: all network calls accept context.Context with deadlines/timeouts.
Modularization: separate mempool ingestion, strategy logic, transaction builder, and signer.
Testability: core arbitrage logic should be pure functions with injected interfaces for RPCs and time.
Secrets: private keys are never in repo, use a secrets manager (Vault / KMS) or env with restricted perms.

6) Concurrency & rate limiting

Ensure tight control over goroutine lifecycle (context cancellation).
Use worker pools for RPCs and bounded channels to avoid OOM.
Rate-limit RPC calls and implement backoff/retry strategies with jitter.

7) Transaction building & signing (critical)

Validate chain ID, EIP-155 protection, correct v,r,s values.
Nonce management: centralize nonce manager; handle failed/non-broadcast txs and re-sync nonces from node on error.
Gas estimation/testing: include sanity checks & max gas limits.
Signing: prefer hardware/remote signers (KMS, HSM). If local private keys used, ensure file perms and encryption.
Replay protection: verify chain ID usage and consider EIP-1559 parameterization (maxFeePerGas/maxPriorityFeePerGas).
If using Flashbots, validate bundle assembly and simulator checks (see Flashbots docs). ([Flashbots Docs][3])

8) Smart-contract audit (if you have custom contracts or interact closely)

Run this toolchain (static + dynamic + fuzz + formal):

Slither for static analysis.
Mythril (symbolic analysis) / MythX for additional SAST.
Foundry (forge) for unit & fuzz tests — Foundry supports fuzzing & is fast. ([Cyfrin][4])
Echidna for property-based fuzzing of invariants. ([0xmacro.com][5])
Consider Manticore/Mythril for deeper symbolic exploration, and Certora/formal approaches for critical invariants (if budget allows). ([Medium][6])

Example solidity workflow:

# slither
slither .

# Foundry fuzzing
forge test --fuzz

# Echidna (property-based)
echidna-test contracts/ --contract MyContract --config echidna.yaml

What to test:

Reentrancy, arithmetic over/underflow (if not using SafeMath / solidity ^0.8), access-control checks, unexpected token approvals, unchecked external calls.

9) Fuzzing — exhaustive plan

Go fuzzing

Use Go’s native fuzz (go test -fuzz) for core libraries (parsers, ABI decoders, bundle builders).
Create fuzz targets focusing on:
- RPC response parsing (malformed JSON).
- ABI decoding and calldata construction.
- Signed-transaction bytes parser.
Run for long durations with corpus seeding from real RPC responses.

Solidity fuzzing

Use Foundry’s forge test --fuzz and Echidna to target invariants (balances, no-negative slippage, token conservation).
Seed fuzzers with historical tx traces & typical call sequences.

Orchestration

Run fuzzers in CI but also schedule long runs in a buildkite/GitHub Actions runner or dedicated machine.
Capture crashes/inputs and convert them to reproducible testcases.

References and how-to guides for solidity fuzzing and Foundry usage. ([Cyfrin][4])

10) Penetration-style tests / adversarial scenarios

Mempool-level adversary: inject malformed or opponent transactions, test how bot reacts to reorgs & chain reorganizations.
Time/latency: simulate delayed RPC responses and timeouts.
Partial failures: simulate bundle reverts, tx replaced, or gas price spikes.
Economic tests: simulate price or liquidity slippage, oracle manipulation scenarios.

11) Monitoring, metrics & observability

Add structured logs (JSON), trace IDs, and use OpenTelemetry or Prometheus metrics for:
- latency from detection → tx submission,
- success/failure counters,
- gas usage per tx,
- nonce mismatches.
Add alerts for repeated reorgs, high failure rates, or sudden profit/loss anomalies.
Simulate alerting (PagerDuty/Slack) in staging to ensure operational readiness.

12) CI/CD & reproducible tests

Integrate all static and dynamic checks into CI:
- gosec, govulncheck, golangci-lint → fail CI on new high/critical findings.
- Unit tests, fuzzing smoke tests (short), Foundry tests for solidity.
Store fuzzing corpora and reproduce minimized crashing inputs in CI artifacts.

13) Secrets & deployment hardening

Never commit private keys or mnemonic in code or config. Use secret manager (AWS KMS / GCP KMS / HashiCorp Vault).
Use least-privilege for node credentials; isolate signer service from other components.
Harden nodes: avoid public shared RPCs for production signing; prefer dedicated provider or local node.

14) Reporting format & remediation plan (what the auditor should deliver)

Executive summary: risk posture and amount at risk.
Prioritized findings: Critical / High / Medium / Low / Informational.
- For each finding: description, evidence (stack trace, log snippets, test reproducer), impact, exploitability, and line-level references.
- Fix recommendation: code patch or test to cover the case; include sample code where relevant.
Re-test plan: how to validate fixes (unit/regression tests, fuzzing seeds).
Follow-up: suggested schedule for re-audit (post-fix) and continuous scanning.

15) Severity guidance (example)

Critical: fund-loss bug; private key compromised; unsigned tx broadcast leak.
High: nonce desync under normal load; reentrancy in helper contract called by bot.
Medium: panic on malformed RPC response; unbounded goroutine leak.
Low: logging missing request IDs; non-idiomatic error handling.
Informational: code style, minor refactors.

16) Example concrete test cases to include

RPC returns truncated JSON → does the bot panic or gracefully retry?
Node returns nonce too low mid-run → does the bot resync or keep retrying stale nonce?
Simulate mempool reordering and reorg of 2 blocks → does bot detect revert & recover?
Flashbots bundle simulator returns revert on one tx → ensure bot doesn’t double-submit other txs.
ABI-decoder fuzzed input causing unexpected panic in Go (fuzzer should find).

17) Example commands / CI snippets (condensed)

# .github/workflows/ci.yml (snippets)
- name: Lint & Security
  run: |
    golangci-lint run ./...
    gosec ./...
    govulncheck ./...

- name: Unit + Race
  run: go test -race ./...

- name: Go Fuzz (short)
  run: go test -run TestFuzz -fuzz=Fuzz -fuzztime=1m ./...

18) Deliverables checklist for the auditor (what to hand back)

Full report (PDF/Markdown) with prioritized findings & diffs/patches.
Repro scripts for each failing case (docker-compose or Foundry/Anvil fork commands).
Fuzzing corpora and minimized crashing inputs.
CI changes / workflow proposals for enforcement.
Suggested runtime hardening & monitoring dashboard templates.

19) Helpful references & toolset (quick links)

gosec — Go security scanner. ([GitHub][1])
govulncheck — Go vulnerability scanner for dependencies. ([Go Packages][2])
Foundry (forge) — fast solidity testing + fuzzing. ([Cyfrin][4])
Echidna — property-based fuzzing for Solidity. ([0xmacro.com][5])
Slither / Mythril — solidity static analysis & symbolic analysis. ([Medium][6])

20) Final notes & recommended audit cadence

Run a full audit (code + solidity + fuzzing) before mainnet launch.
Keep continuous scanning (gosec/govulncheck) in CI on every PR.
Schedule quarterly security re-checks + immediate re-audit for any major dependency or logic change.

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