# Symbolic Execution Tools Module

**Purpose:** Symbolic execution engine and security analysis

## Overview

This module provides symbolic execution capabilities for analyzing program behavior and detecting security vulnerabilities through taint analysis.

## Core Components

### engine.py (19,651 LOC)
**Symbolic execution engine**

```python
class SymbolicExecutionEngine:
    def execute(self, code: str, max_paths: int = 10) -> SymbolicResult:
        """Symbolically execute code to explore all paths."""
        pass
```

**Features:**
- Z3 constraint solver integration
- Path explosion mitigation (bounded unrolling)
- State forking and merging
- Type support: Int, Bool, String, Float
- Loop unrolling with fuel limits

### security_analyzer.py (36,022 LOC)
**Security vulnerability detection**

```python
class SecurityAnalyzer:
    def analyze(self, code: str) -> SecurityResult:
        """Detect security vulnerabilities via taint analysis."""
        pass
```

**Detects (OWASP Top 10):**
- SQL Injection (CWE-89)
- NoSQL Injection (CWE-943)
- Cross-Site Scripting (CWE-79)
- Command Injection (CWE-78)
- Path Traversal (CWE-22)
- LDAP Injection (CWE-90)
- XXE - XML External Entity (CWE-611)
- SSTI - Server-Side Template Injection (CWE-1336)

### taint_tracker.py (104,118 LOC) - **LARGEST MODULE**
**Data flow and taint tracking**

```python
class TaintTracker:
    def track_taint(self, source: Node, sink: Node) -> List[TaintPath]:
        """Track taint propagation from source to sink."""
        pass
```

**Taint Levels:**
- `UNTAINTED` - Safe data
- `LOW` - User input from authenticated users
- `MEDIUM` - External API data
- `HIGH` - Direct user input
- `CRITICAL` - Unauthenticated external input

**Key Features:**
- Interprocedural taint tracking
- Taint propagation rules
- Sanitizer detection
- Source-sink analysis

### vulnerability_scanner.py (23,106 LOC)
**Pattern-based vulnerability scanning**

Complements taint analysis with pattern matching:
- Hardcoded credentials detection
- Weak cryptography detection (MD5, SHA-1)
- Dangerous function usage (`eval`, `exec`, `pickle.load`)
- SQL injection patterns

## Advanced Components

### cross_file_taint.py (49,520 LOC)
**Cross-file taint tracking**

Tracks taint flow across module boundaries:
```python
# routes.py
user_input = request.json['data']  # Source
process_data(user_input)

# utils.py
def process_data(data):
    cursor.execute(f"SELECT * FROM users WHERE id = {data}")  # Sink
```

### unified_sink_detector.py (40,109 LOC)
**Polyglot sink detection**

Unified sink detection across languages:
- Python: `execute()`, `eval()`, `os.system()`
- Java: `executeQuery()`, `Runtime.exec()`
- JavaScript: `eval()`, `innerHTML =`
- TypeScript: Same as JavaScript

### type_evaporation_detector.py (24,503 LOC)
**TypeScript type evaporation detection**

Detects when TypeScript compile-time types evaporate at runtime:
```typescript
type Role = 'admin' | 'user'
const role = input.value as Role  // ❌ Type assertion without validation
```

## Security-Focused Components

### secret_scanner.py (16,651 LOC)
**Hardcoded secret detection**

Detects 30+ patterns:
- API keys
- Passwords
- Private keys
- AWS credentials
- Database connection strings
- OAuth tokens

### frontend_input_tracker.py (31,480 LOC)
**Frontend input tracking**

Tracks user input in web frontends:
- DOM input elements
- Form submissions
- Event handlers
- React component props

### grpc_contract_analyzer.py (24,709 LOC)
**gRPC contract analysis**

Analyzes gRPC service definitions and implementations:
- Protocol buffer analysis
- Service method verification
- Request/response validation

### graphql_schema_tracker.py (39,854 LOC)
**GraphQL schema analysis**

Tracks GraphQL schema and resolvers:
- Schema definition analysis
- Resolver validation
- Query complexity analysis

### kafka_taint_tracker.py (41,727 LOC)
**Kafka message taint tracking**

Tracks taint through Kafka message queues:
- Producer taint sources
- Consumer taint sinks
- Message serialization boundaries

### schema_drift_detector.py (33,169 LOC)
**Database schema drift detection**

Detects when code assumptions don't match database schema:
```python
# Code expects: users.email (string)
# Database has: users.email_address (string)
```

## Specialized Analysis

### ir_interpreter.py (42,392 LOC)
**IR-based symbolic execution**

Executes unified IR symbolically (language-agnostic):
```python
class IRInterpreter:
    def interpret(self, ir: IRNode) -> SymbolicState:
        """Interpret IR symbolically."""
        pass
```

### state_manager.py (10,460 LOC)
**Symbolic state management**

Manages symbolic execution state:
- Variable bindings
- Constraints
- Path conditions
- State forking/merging

### constraint_solver.py (11,867 LOC)
**Z3 constraint solver wrapper**

Wraps Z3 for constraint solving:
```python
class ConstraintSolver:
    def solve(self, constraints: List[Constraint]) -> Solution:
        """Solve constraints using Z3."""
        pass
```

### type_inference.py (12,243 LOC)
**Symbolic type inference**

Infers types during symbolic execution:
```python
x = "hello"  # x: string
y = len(x)   # y: int
```

## Usage

```python
from code_scalpel.symbolic_execution_tools import (
    SymbolicExecutionEngine,
    SecurityAnalyzer,
    TaintTracker
)

# Symbolic execution
engine = SymbolicExecutionEngine()
result = engine.execute(code, max_paths=10)
for path in result.paths:
    print(f"Path {path.path_id}: {path.reproduction_input}")

# Security analysis
analyzer = SecurityAnalyzer()
security_result = analyzer.analyze(code)
if security_result.has_vulnerabilities:
    for vuln in security_result.vulnerabilities:
        print(f"{vuln.type} at line {vuln.line}: {vuln.description}")

# Taint tracking
tracker = TaintTracker()
taint_flows = tracker.track_taint(source_node, sink_node)
for flow in taint_flows:
    print(f"Taint path: {' -> '.join(flow.nodes)}")
```

## Integration

Used by:
- `mcp/server.py` - MCP tools: `security_scan`, `symbolic_execute`, `cross_file_security_scan`
- `generators/` - Test generation from symbolic paths
- `policy_engine/` - Security policy enforcement
- `autonomy/` - Automated vulnerability fixing

## v3.0.5 Status

- Engine: Stable, 100% coverage
- Security analyzer: Stable, 100% coverage
- Taint tracker: Stable, 100% coverage
- Cross-file taint: Stable, 95% coverage
- Unified sink detector: Stable, 95% coverage
- All specialized analyzers: Stable, 85-95% coverage

**Note:** This is the largest module in Code Scalpel, containing 500K+ LOC of analysis code.

---

## 🚀 Enhancement Roadmap (v3.3.0 - v3.5.0)

### Planned Enhancements

#### Core Engine Improvements (v3.3.0)

**engine.py - Symbolic Execution Engine**
- [ ] Complex data type support (List, Dict, Tuple, Set)
- [ ] Concolic execution (hybrid concrete + symbolic)
- [ ] Improved path exploration strategies (DFS, BFS, best-first)
- [ ] Coverage-guided exploration
- [ ] Path memoization to avoid re-exploring
- [ ] Incremental constraint solving
- [ ] Parallel path exploration
- [ ] Loop widening for unbounded loops
- [ ] Loop invariant detection
- [ ] Dynamic unrolling based on complexity

**constraint_solver.py - Constraint Solving**
- [ ] Alternative SMT solvers (CVC5, MathSAT5, Yices2)
- [ ] Solver selection strategy (fastest for problem type)
- [ ] Constraint minimization
- [ ] MaxSMT for optimization problems
- [ ] UNSAT core extraction for debugging
- [ ] Incremental solving with push/pop
- [ ] Quantifier support (forall/exists)
- [ ] Result caching with constraint hashing
- [ ] Constraint subsumption checking

**symbolic_memory.py - Memory Model** *(NEW STUB)*
- [ ] Symbolic arrays with symbolic indices
- [ ] Symbolic dictionaries with symbolic keys
- [ ] Object field access with symbolic paths
- [ ] Heap memory tracking (for C/C++)
- [ ] Buffer overflow detection
- [ ] Use-after-free detection
- [ ] Memory leak detection
- [ ] Z3 array theory integration

#### Security Analysis Enhancements (v3.3.0)

**taint_tracker.py - Taint Tracking**
- [ ] WebSocket and SSE as taint sources
- [ ] Redis/Memcached cache sources
- [ ] Message queue sources (RabbitMQ, SQS)
- [ ] GraphQL subscription sources
- [ ] Context-aware taint propagation (authentication reduces taint)
- [ ] Taint decay modeling
- [ ] Object-sensitive tracking (track fields separately)
- [ ] Path-sensitive analysis
- [ ] Async/await taint propagation

**vulnerability_scanner.py - Dependency Scanning**
- [ ] Additional vulnerability databases (NVD, Snyk, GitHub Advisories)
- [ ] CISA KEV (Known Exploited Vulnerabilities)
- [ ] Vendor-specific advisories (Red Hat, Ubuntu)
- [ ] Transitive vulnerability propagation
- [ ] Reachability analysis (is vulnerable code actually called?)
- [ ] License compliance checking
- [ ] Supply chain attack detection
- [ ] SBOM generation and validation
- [ ] Auto-generate fix PRs
- [ ] Risk score calculation (CVSS + exploitability)

**unified_sink_detector.py - Polyglot Sinks**
- [ ] Supply chain injection sinks (npm install scripts)
- [ ] Container escape vectors
- [ ] Secrets in logs detection
- [ ] Timing attack patterns
- [ ] Memory disclosure patterns
- [ ] Framework-specific sinks (Django ORM, Rails, Spring, Flask)
- [ ] Cloud-native security sinks (AWS SDK, Kubernetes API)
- [ ] AI/ML vulnerabilities (model poisoning, prompt injection)

**sanitizer_analyzer.py - Sanitizer Validation** *(NEW STUB)*
- [ ] Incomplete sanitization detection
- [ ] Context mismatch detection (HTML escaping for SQL)
- [ ] Bypassable sanitizer identification
- [ ] Known bypass technique testing
- [ ] Symbolic execution of sanitizers
- [ ] Attack payload generation
- [ ] Sanitizer effectiveness scoring
- [ ] Auto-fix recommendations

#### Cross-Boundary Analysis (v3.3.0)

**cross_file_taint.py - Multi-File Analysis**
- [ ] Circular import handling
- [ ] Decorator and metaclass taint tracking
- [ ] Class hierarchy taint analysis
- [ ] Dynamic import support (importlib)
- [ ] Cross-language taint (Python ↔ C/C++, Python ↔ Java)
- [ ] Polyglot microservices (REST/gRPC boundaries)
- [ ] Framework-aware chains (Django request → view → template)
- [ ] Demand-driven analysis (only analyze reachable paths)
- [ ] Call graph pruning

**kafka_taint_tracker.py - Message Queue Taint**
- [ ] RabbitMQ (AMQP) support
- [ ] Redis Pub/Sub and Streams
- [ ] Apache Pulsar
- [ ] AWS SQS/SNS
- [ ] Google Cloud Pub/Sub
- [ ] Azure Service Bus
- [ ] Avro schema taint mapping
- [ ] Protobuf field-level taint
- [ ] Topic ACL violation detection
- [ ] Schema registry validation

**type_evaporation_detector.py - Type Boundary Analysis**
- [ ] Zod/Yup validation detection
- [ ] io-ts runtime type validation
- [ ] JSON Schema validation at endpoints
- [ ] class-validator decorators (NestJS)
- [ ] tRPC type safety verification
- [ ] GraphQL Code Generator checks
- [ ] Type guard usage validation (is, typeof, instanceof)
- [ ] Discriminated union exhaustiveness

#### API Contract Analysis (v3.3.0)

**grpc_contract_analyzer.py - gRPC Analysis**
- [ ] Missing authentication interceptors
- [ ] Unencrypted metadata (credentials leak)
- [ ] TLS configuration validation
- [ ] Rate limiting gap detection
- [ ] Timeout configuration enforcement
- [ ] Retry policy validation
- [ ] Field number reuse detection (breaking change)
- [ ] Large message size warnings
- [ ] Pagination detection

**graphql_schema_tracker.py - GraphQL Analysis**
- [ ] Query depth attack detection
- [ ] Query complexity limits
- [ ] Rate limiting configuration
- [ ] Introspection disabled in production
- [ ] Authentication directive validation (@auth)
- [ ] N+1 query detection
- [ ] DataLoader usage checks
- [ ] Federation support (@key directive)

**schema_drift_detector.py - Schema Evolution**
- [ ] Avro schema evolution rules
- [ ] Thrift IDL change detection
- [ ] AsyncAPI specification support
- [ ] CloudEvents schema validation
- [ ] Automated migration script generation
- [ ] Impact analysis (affected clients)
- [ ] Deployment risk scoring
- [ ] Compatibility level validation

#### Advanced Features (v3.4.0)

**concolic_engine.py - Hybrid Execution** *(NEW STUB)*
- [ ] Concrete + symbolic execution
- [ ] Path negation and re-execution
- [ ] Input generation from negated constraints
- [ ] Coverage tracking
- [ ] Handle complex operations (crypto, regex) concretely
- [ ] Iterative deepening search
- [ ] State serialization for resumable analysis
- [ ] Automatic symbolic ↔ concolic fallback

**path_prioritization.py - Smart Exploration** *(NEW STUB)*
- [ ] Coverage-guided prioritization
- [ ] Security-focused prioritization (prefer paths to sinks)
- [ ] Complexity-based prioritization (simple paths first)
- [ ] Historical data-driven prioritization
- [ ] Distance to target metric
- [ ] Path constraint complexity scoring
- [ ] Adaptive strategy switching
- [ ] Multi-armed bandit for strategy selection

**ml_vulnerability_predictor.py - ML-Based Prediction** *(NEW STUB)*
- [ ] AST structural features
- [ ] Code metrics (complexity, LOC, nesting)
- [ ] Taint flow features
- [ ] Historical features (churn, bug count)
- [ ] Random Forest baseline
- [ ] Gradient Boosting (XGBoost)
- [ ] Graph Neural Networks for AST
- [ ] CVE database scraping
- [ ] GitHub Security Advisories integration
- [ ] SHAP explainability
- [ ] Real-time prediction during analysis

**frontend_input_tracker.py - Frontend Analysis**
- [ ] Solid.js support
- [ ] Qwik support
- [ ] Lit web components
- [ ] Alpine.js
- [ ] Preact
- [ ] Ember.js
- [ ] innerHTML/outerHTML XSS tracking
- [ ] dangerouslySetInnerHTML detection
- [ ] Client-side routing vulnerabilities
- [ ] localStorage XSS chains

**secret_scanner.py - Secret Detection**
- [ ] Base64-encoded secrets
- [ ] Hex-encoded credentials
- [ ] Encrypted secrets without key management
- [ ] Azure connection strings
- [ ] GCP service account keys
- [ ] DigitalOcean tokens
- [ ] Cloudflare tokens
- [ ] Shannon entropy analysis
- [ ] Weak secret patterns
- [ ] Version control history scanning
- [ ] Secret rotation age validation

---

## 📊 Enhancement Metrics

### Current Coverage
- **13 production modules**: 500K+ LOC, 85-100% test coverage
- **OWASP Top 10 2021**: Complete coverage
- **Languages**: Python, Java, TypeScript, JavaScript
- **Frameworks**: 15+ supported (React, Vue, Django, Flask, Spring, etc.)

### Planned Additions (v3.3.0 - v3.5.0)
- **5 new modules**: 50K+ LOC planned
- **100+ new features**: Across existing modules
- **20+ new vulnerability types**: Supply chain, cloud-native, AI/ML
- **10+ new frameworks**: Solid, Qwik, Lit, Alpine, etc.
- **5 new SMT solvers**: CVC5, MathSAT5, Yices2, etc.

### Priority Levels
🔴 **HIGH** (v3.3.0 - Q1 2026):
- Concolic execution
- Symbolic memory model
- Sanitizer analyzer
- Path prioritization

🟡 **MEDIUM** (v3.4.0 - Q2 2026):
- ML vulnerability predictor
- Enhanced cross-language taint
- Additional message brokers

🟢 **LOW** (v3.5.0 - Q3 2026):
- Alternative SMT solvers
- Advanced frontend frameworks
- Extended API contract analysis

---

## Module Enhancement Roadmap by Tier

### COMMUNITY (Current & Planned)
- Core security analysis improvements
- Documentation enhancements
- Example scripts and tutorials
- Troubleshooting guides
- Performance benchmarking

### PRO (Enhanced Features)
- Advanced vulnerability detection
- Framework-specific analysis
- Incremental analysis support
- Polyglot taint tracking
- Custom patterns and rules
- Real-time analysis mode
- IDE integrations

### ENTERPRISE (Advanced Capabilities)
- Distributed analysis across clusters
- Machine learning-based detection
- Custom vulnerability databases
- Multi-tenant support
- Compliance auditing
- SIEM integration
- Real-time monitoring
- Advanced visualization
- Automated remediation

---

## 🤝 Contributing

To contribute to these enhancements:

1. **Pick a TODO**: Choose from the lists above
2. **Create stub if needed**: Follow patterns in new stub files
3. **Write tests first**: TDD approach preferred
4. **Implement incrementally**: Small PRs, frequent merges
5. **Update README**: Document new features

See [CONTRIBUTING.md](../../../../CONTRIBUTING.md) for development guidelines.

---

**Last Updated**: December 24, 2025 (Tier-organized TODOs added)  
**Version**: v3.0.5 (current) → v3.5.0 (planned)
