Scaling Smart Contract Security with AI I Shashank

Shashank, founder of SolidityScan, explains how AI and automation are transforming smart contract security. With over 14 years in cybersecurity (credited with finding bugs in Apple, Google, and Facebook), he highlights the staggering scale of Web3 hacks—over $5B in 2024, with 55% caused by contract vulnerabilities—and argues that manual audits alone can’t scale. He demonstrates how AI-powered automation reduces false positives and identifies nuanced access-control issues.

Using the 2022 ShadowFi token hack as a case study, Shashank shows how traditional rule-based scanners struggle with logic-based bugs, while AI understands contextual security patterns. He details how SolidityScan’s multi-agent AI model, trained on 100K+ audit reports, mimics an “AI auditor” in developers’ CI/CD pipelines—reducing false positives by 82% and enabling real-time vulnerability detection before deployment.

00:02 Introduction & context: AI + blockchain security

00:27 Shashank’s background in cybersecurity (Apple, Google, Facebook)

00:59 Web3 security challenges: hacks & financial losses

01:28 Birth of SolidityScan: AI-powered contract analysis

02:00 Why automation is needed: billions lost, manual audits can’t scale

02:45 CI/CD security comparison to Web2

03:09 Rule-based scanning limitations → need for AI

03:27 Case study: ShadowFi token hack (Sept 2022)

04:10 Transaction analysis: token burn exploit explained

05:21 Access control vulnerabilities as #1 cause

05:44 How missing modifiers caused the exploit

06:08 Why rule-based systems fail to catch custom RBAC patterns

06:59 Example: false positives in static scanning

07:32 Using AI to understand code context & access logic

08:05 Results: 82% reduction in false positives

08:57 Multi-agent AI: master/slave setup & specialized agents

09:30 Dataset: 100K+ audit reports, 1M+ vulnerabilities

10:01 How the AI auditor works in developer pipelines

10:25 Audience Q&A: context limits, hallucinations, AI-guided algorithms

11:50 Future plans: formal verification & AI-generated fuzzing tests

12:10 Static analysis today, dynamic verification next

12:43 Closing remarks

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