Wake

Four AI auditing tools. Fourteen protocols. The shape tells the story. Wake Arena 3.1's blue area contains the other three on every axis. Where all systems struggle — Burve, Notional — the gap closes. Where the work matters — Lend, Munchables, Virtuals — the edge is visible and consistent. What the chart doesn't show: 49 of the 63 found vulnerabilities were confirmed by more than one Wake Arena agent independently. The shape reflects consensus, not a single model's guess. Learn more ↓

Labels are from Code4rena and Sherlock, taken as ground truth. Some may have been miscategorized, a known limitation of any competition-based benchmark. • Follow @WakeFramework for protocol-by-protocol breakdowns • Repost to help the security community calibrate AI audit expectations Benchmarks: github.com/Ackee-Blockcha… Try Wake Arena: ackee.xyz/wake/arena

Most tools give you a headline number. Here's every number. 9 models × 14 protocols = 126 cells. Wake Arena 3.1 is the darkest row. But look at where all systems struggle: Burve, Notional Exponent, Lambo.win. No tool finds everything, and this chart makes that visible. On Lend, the hardest protocol in the dataset with 28 high-severity findings: Wake Arena 3.1 found 20. Zellic v12 found 10. Learn more ↓

Why use Wake's extension? Real-time vulnerability detection as you type: Same detectors used in Lido, Aave, Axelar, Safe audits. Red squiggles for security issues, not just syntax errors. Only extension with proven audit framework integration.

Graph-driven reasoning. Multi-agent cross-checking. Static analysis context from 200+ audits. That's the architecture behind the step change. • Follow @WakeFramework for technical breakdowns • Repost if you build or secure smart contracts Benchmarks: github.com/Ackee-Blockcha… Try Wake Arena: ackee.xyz/wake/arena

The jump from Wake Arena 3.0 to 3.1 is a +21 percentage point increase in detection rate. 3.0 at 46% → 3.1 at 67% Frontier LLMs plateau around 44%. That ceiling is real: more compute, better prompts, same result. The staircase: Opus 4.5 (22%) GPT-5 (26%) GPT-5.2 xhigh (44%) = Zellic v12 (44%) Wake Arena 3.0 (46%) Wake Arena 3.1 (67%). Breaking through that ceiling required a different architecture. Not a different prompt. Learn more ↓

4/ Access control bugs and incorrect guard logic appear consistently in production audits. Testing the revert path is the check most teams keep skipping. • Follow @WakeFramework for more insights • Repost to help others Get Wake: getwake.io

3/ Extend it: assert the specific revert reason. After the block, check the captured error to confirm the correct reason was returned — not just that something reverted. Tests that verify the right error catch guard logic bugs, not just guard existence.

Access control tests that only verify the authorized caller leave the unauthorized caller untested. Wake's must_revert() makes revert testing as clean as success testing. Here's the pattern: Thread ↓

Congrats! Developers keep voting with their time: better Solidity tooling wins. The IDE space is still underserved. Most extensions handle syntax and compilation. Few integrate security into the writing experience. Fewer still connect analysis to a full fuzzing pipeline. Wake's VS Code extension was built from the same frustration. Vulnerability detection on every keystroke, powered by detectors from 200+ production audits. Inheritance graphs, call flows, Remix-like testing, all without leaving the editor. 44,000+ installs. What a first-place finish for a Solidity IDE proves: the market rewards integrated developer experience. That signal matters across the whole ecosystem.

Audit access is one barrier. Code readiness is the other. Most teams arrive at their first audit with surface-level bugs that burn expensive reviewer hours. Reentrancy, missing checks, storage collisions. Mechanical issues that tools should catch before a human ever reads the code. The pattern that works: static analysis and targeted fuzzing before submission. Wake Arena runs multi-agent AI + 108 detectors from 200+ audits, property-based fuzzing on pytest, and AI-driven analysis. All before an auditor opens the repo. Arbitrum builders applying to this program should ask what state their code is in first. Cleaner submissions mean faster turnarounds and sharper findings where it counts, deep protocol logic.

@Priscilla_nuel You clearly get it. Appreciate your comment. Meanwhile, as we are growing our community, a follow from you would mean a lot. Thanks in advance.

@WakeFramework Achieving an audited and secure smart contract is big business and everyone's business.

Security issues don't announce themselves in the file you're currently editing. Wake's Security Overview panel surfaces every detected vulnerability across your whole project — severity-sorted, always current, no terminal required. Thread ↓

@abhi3700 Well done, solid work! I dropped you a DM with some open-source tooling you may find interesting, given your work.

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4/ Same detectors used in Ackee audits securing Lido, Aave, Axelar, Safe. Not syntax warnings. Vulnerability detection from the same framework behind 200+ audits. • Follow @WakeFramework for more insights • Repost to help others Get the Solidity extension: marketplace.visualstudio.com/items?itemName…

3/ One prerequisite: your project needs to compile cleanly. Once it does, the panel runs continuous analysis on every file in your workspace — not just the one that's open. Fix compilation errors first, then expect the panel to populate.

@0xVoid_Web3 Exactly! We are proud to say that Wake Arena achieves: True positive rate >70% False positive rate <30% I appreciate your comment, and as we are growing our community, a follow from you would mean a lot. Thanks in advance.

@WakeFramework Signal > noise that's the real challenge in automated auditing

Impressive benchmark, and it highlights where the industry is heading. Raw LLMs can identify known vulnerabilities. But production security demands more: structured, multi-step validation, domain-specific reasoning, and false-positive control. A single prompt to Claude or GPT catches some bugs. It also produces an 86%+ false positive rate on real Solana codebases. Wake Arena takes a different approach. Multi-agent AI with graph-driven reasoning through Data Dependency Graphs, fed by 108 battle-tested detectors from audits securing $180B+ in TVL. On Printr Protocol, it caught 5 critical vulnerabilities plus 17 additional issues before deployment. The question isn't whether AI can find bugs. It's whether it can do so reliably enough to act on.