Scaling Smart Contract Security with AI

on YouTube ↗13m

" summary

Claims that AI-driven automation cuts static scanner noise, citing a 82% reduction in false positives after training on 100K+ audit reports and 1M+ labeled vulnerabilities. Demonstrates the Sept 2022 ShadowFi token burn exploit where rule-based scanners miss custom RBAC and public burn functions, then shows a deployed multi-agent master/slave model that embeds an “AI auditor” into CI/CD for real-time detection; current system is static analysis with plans for formal verification and AI-generated fuzzing.

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" chapters (22)

1. 0:00Start
2. 0:02Introduction & context: AI + blockchain security
3. 0:27Shashank’s background in cybersecurity (Apple, Google, Facebook)
4. 0:59Web3 security challenges: hacks & financial losses
5. 1:28Birth of SolidityScan: AI-powered contract analysis
6. 2:00Why automation is needed: billions lost, manual audits can’t scale
7. 2:45CI/CD security comparison to Web2
8. 3:09Rule-based scanning limitations → need for AI
9. 3:27Case study: ShadowFi token hack (Sept 2022)
10. 4:10Transaction analysis: token burn exploit explained
11. 5:21Access control vulnerabilities as #1 cause
12. 5:44How missing modifiers caused the exploit
13. 6:08Why rule-based systems fail to catch custom RBAC patterns
14. 6:59Example: false positives in static scanning
15. 7:32Using AI to understand code context & access logic
16. 8:05Results: 82% reduction in false positives
17. 8:57Multi-agent AI: master/slave setup & specialized agents
18. 9:30Dataset: 100K+ audit reports, 1M+ vulnerabilities
19. 10:01How the AI auditor works in developer pipelines
20. 10:25Audience Q&A: context limits, hallucinations, AI-guided algorithms
21. 11:50Future plans: formal verification & AI-generated fuzzing tests
22. 12:10Static analysis today, dynamic verification next

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