AI-Powered Code Security Analysis
Yash Oza
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.
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 (Ā±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
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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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
