Pre-Audit Readiness Engine

A pipeline that simulates a senior security researcher's first pass on a smart contract repo to catch the mechanical and process findings that would otherwise inflate a paid audit by ~30%.

A pre-audit readiness engine is a tooling layer that runs before a paid manual audit, with one purpose: catch the findings and process gaps that should never reach a senior auditor's billable hour. It is not a static analyzer like Slither, not a single-pass LLM scanner, and not a replacement for a manual review. It sits between those tools and the human audit and is designed to lower the cost of the human audit by raising the quality of the input.

Why It Exists

Auditors charge for the time it takes to understand your code. Conservatively, 30% of an audit bill is a tax on unpreparedness: reverse-engineering intent, deciphering undocumented architecture, triaging untriaged Slither warnings, dealing with broken commits during the engagement. A pre-audit readiness engine targets that 30% directly.

What It Has To Do

Detect like an auditor, not like a pattern matcher. Reason about whether a flagged pattern is actually exploitable in the code's context, not just whether it matches a regex.
Eliminate false positives. A noisy report is worse than no report — teams learn to skim past warnings, and the real bug gets missed. Effective readiness engines run an explicit false-positive elimination layer (Krait calls these Kill Gates) that tries to disprove every candidate finding.
Cover process gaps that code analysis cannot see. Frozen commit. scope.txt. NatSpec coverage. Invariant list. Branch coverage report. These do not appear in any static analyzer's output but are the single largest cost driver in real audits.

How It Differs From Adjacent Tools

Tool class	What it does	What it does not do
Static analyzer (Slither, Aderyn)	Emits warnings against fixed AST patterns	Reason about exploitability, suppress noise, cover docs/process
Single-pass LLM scanner	One LLM read over the code, lists possible issues	Disprove its own findings, reproduce on shadow audits
Manual audit	Human researcher finds economic and logic bugs	Run on every PR, scale to a 24h iteration loop
Pre-audit readiness engine	All of the above as a structured pipeline	Replace the manual audit on economic/business-logic bugs

Krait As The Reference Implementation

Krait is the Zealynx implementation. It runs a 4-phase pipeline (Detector → Reasoner → Critic → Ranker), is benchmarked publicly against 40 Code4rena / Sherlock / CodeHawks contests via shadow audits, and reached 100% precision at 0.2 FPs per contest in its most recent five-contest cycle. The methodology and pattern library are open-source at github.com/ZealynxSecurity/krait. Full breakdown in the pre-audit readiness engine deep dive.

When To Use One

Before paying for the first audit on a new protocol.
Before each follow-up audit on a maintained protocol — the readiness score gives the auditor an updated context map.
Inside the engineering CI loop on every PR that touches contracts, to catch regressions before they reach the freeze.
Before a tier-1 exchange listing or institutional integration that requires an audit history.

Related Concepts

Audit Readiness — the state of the codebase; a pre-audit readiness engine measures and improves it.
Shadow Audit — the public benchmark format used to validate that a readiness engine catches real bugs.
Audit Scope — what a readiness engine helps you define and lock before the engagement begins.

Articles Using This Term

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