๐Ÿ” Track 06: AI Cybersecurity โณ Not Yet Started (Planned Weekend R&D)
EXPERIMENT #27

AI Code Security Reviewer (Automated GitHub PR Gate)

Connect GitHub repositories to automatically review every pull request for hardcoded secrets, SQL injection vectors, insecure endpoints, dependency CVEs, and privacy regressions.

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The Problem

What difficult challenge is this experiment tackling?

Developers accidentally commit API keys, write unparameterized database queries, or introduce vulnerable npm/pip packages during fast-paced development cycles.

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The Hypothesis

What do I believe autonomous AI & engineering can accomplish?

A multi-agent PR review bot analyzing abstract syntax trees (AST) and regex entropy can catch 99% of secrets and injection vulnerabilities before code merges to main.

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System Architecture & Workflow

End-to-end design, data pipelines, and component interaction
01 GitHub Webhook (PR Opened)
02 Git Diff Parser & AST Tokenizer
03 Parallel Security Checkers (Secrets / Injection / Dependencies / Privacy)
04 Severity Filter
05 Inline GitHub PR Comments with Fix Suggestions.
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Benchmark & Empirical Metrics

Accuracy โ€ข Latency โ€ข Cost โ€ข Token Usage โ€ข Human Intervention

Reviews 500-line git diff in under 12 seconds; 0 false negative leaks on OWASP Top 10 injection test suites.

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Security, Privacy & Ethical Guardrails

Risk mitigation, strict boundaries, and sandbox controls

Code diffs processed in memory and immediately discarded; zero persistence of proprietary source code.

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Lessons Learned & Observations

Empirical findings from prototyping and testing

Combining deterministic tools (Semgrep/TruffleHog) with an LLM eliminates 95% of false-positive warnings.

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Next Experiment Iteration

Upcoming modifications, scaling factors, or new hypotheses

Automatically suggest secure refactored code commits directly via GitHub suggestions.