AI-Powered Code Security Analysis

Yash Oza

Completed work

AI Agent Designer

Security Engineer

AI Engineer

Google Gemini

Python

Artificial Intelligence

Multi-Agent Analysis Architecture

Sophisticated AI agents working in harmony to ensure comprehensive security analysis

Intelligently scans and filters code repositories, identifying target files and ignoring irrelevant dependencies

Multi-layered security analysis for comprehensive code review

Leverages Abstract Syntax Trees for precise structural analysis, detecting complex security vulnerabilities that regex-based tools miss. Analyzes code semantics, not just patterns.

Sophisticated data flow analysis tracking potentially dangerous user inputs through complex application flows, identifying injection vulnerabilities and unsafe data handling.

Built with cutting-edge tools and frameworks for maximum performance and reliability

Systematic approach to comprehensive code security review

Advanced repository parsing with intelligent file filtering, dependency exclusion, and support for multiple programming languages and frameworks.

Key Features:

Intelligent file filtering based on supported extensions

Automatic exclusion of irrelevant directories (.git, node_modules, __pycache__)

Environment file exclusion (.env) for security

Support for 15+ languages including Python, JavaScript, Java, C++, Go, Rust

Cross-platform compatibility with pathlib

Deep structural code analysis using Abstract Syntax Trees to identify security vulnerabilities with surgical precision.

Capabilities:

Dangerous function detection (eval, exec, pickle)

SQL injection pattern recognition

Subprocess command injection analysis

Syntax error detection

Import and dependency tracking

Integrates multiple static analysis engines including Bandit, detecting known security vulnerabilities and compliance issues.

Features:

200+ built-in security rules

CWE classification for vulnerabilities

JSON output processing

Timeout handling for large codebases

Automatic tool availability detection

Sophisticated data flow analysis tracking potentially dangerous user inputs through complex application flows.

Analysis Types:

User input tracking (web requests, CLI, environment)

Sanitization function detection

Dangerous sink identification

Variable propagation analysis

Cross-function taint tracking

Validates findings across multiple analysis methods to eliminate false positives and increase confidence scores.

Validation Methods:

Fuzzy line number matching (±5 lines)

Source correlation (AST vs Bandit)

Confidence level adjustment

Issue grouping and deduplication

Agreement rate calculation

Google Gemini-powered analysis for business logic flaws and context-aware security recommendations.

AI Capabilities:

Business logic flaw detection

Authentication bypass patterns

Cryptographic implementation flaws

Session management vulnerabilities

Context-aware issue reporting

Easy integration with your existing development workflow and CI/CD pipelines

# Install required dependencies
pip install ast bandit google-generativeai

# Set environment variables
export GEMINI_API_KEY="your_api_key_here"

# Run the code review
python code_review_bot.py /path/to/your/repository

# Sample output
🚀 Starting code review workflow...
🔍 Parsing files in: /path/to/your/repository
📁 Found 42 files to review
🌳 Running AST-based security analysis...
🌳 AST analysis found 15 issues
🛡️ Running Bandit security analysis...
🛡️ Bandit found 22 security issues
🔍 Cross-validating issues...
📊 Validation: 18 agreements, 3 AST-only, 4 Bandit-only
🔬 Running enhanced taint analysis...
🔬 Taint analysis found 7 data flow issues
🧠 Running Gemini semantic analysis...
🧠 Gemini found 5 additional semantic issues
🚀 Code review workflow completed
🔍 Code review completed for /path/to/your/repository
Total issues found: 49
AST issues: 15
Bandit issues: 22
Gemini issues: 5
Taint issues: 7

Experience our AI-powered code review system in action

Critical SQL Injection Vulnerability

Suggested Fix: Use parameterized queries

Sources: AST, Bandit, Taint Analysis (Confidence: High)

Major Unsafe Deserialization

Detected at line 87: pickle.loads() called with user-controlled data

Suggested Fix: Use JSON for safe serialization or implement strict validation

Sources: AST, Gemini AI (Confidence: Medium)

Minor Hardcoded Credentials

Detected at line 15: API key hardcoded in source file

Suggested Fix: Use environment variables for sensitive data

Sources: Bandit, Gemini AI (Confidence: High)

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Completed work

Posted Jul 9, 2025

AI-powered code review system combining AST parsing, static analysis, taint tracking & AI semantic review.

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Timeline

Jul 1, 2025 - Jul 5, 2025

AI-Powered Code Security Analysis

Multi-Agent Analysis Architecture

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