Introduction: Why Security Boundaries Matter in AI Code Refactoring
As large language models become integral to automated code refactoring workflows, establishing robust security boundaries is no longer optional—it's mission-critical. When I first implemented AI-assisted refactoring at scale, a single unchecked eval() call in generated code nearly compromised our entire CI/CD pipeline. That incident catalyzed my deep dive into security boundary patterns for LLM-based code transformation.
In this comprehensive guide, I'll walk you through implementing production-grade security boundary checks that let you harness AI's refactoring power while maintaining strict safety guarantees. We'll use HolySheep AI as our unified API gateway, which delivers sub-50ms latency at dramatically reduced costs compared to direct provider APIs.
2026 LLM Pricing Landscape: Cost Analysis for High-Volume Refactoring
Before diving into implementation, let's examine the current pricing reality for production refactoring workloads. Understanding costs is essential for capacity planning and ROI justification.
| Model | Output Price ($/MTok) | 10M Tokens/Month | Annual Cost |
|---|---|---|---|
| GPT-4.1 | $8.00 | $80,000 | $960,000 |
| Claude Sonnet 4.5 | $15.00 | $150,000 | $1,800,000 |
| Gemini 2.5 Flash | $2.50 | $25,000 | $300,000 |
| DeepSeek V3.2 | $0.42 | $4,200 | $50,400 |
HolySheep AI operates at ¥1=$1 rate, delivering approximately 85%+ savings compared to the standard ¥7.3+ rates on direct API purchases. For a typical 10M token/month workload running DeepSeek V3.2, you're looking at:
- Direct Provider Cost: $4,200/month
- HolySheep AI Cost: ~$630/month (85% reduction applied)
- Annual Savings: $42,840/year
Beyond cost, HolySheep supports WeChat and Alipay for seamless payment, offers free credits on signup, and maintains consistent <50ms latency through their optimized relay infrastructure.
Architecture Overview: Security Boundary Pattern
Our security boundary architecture implements defense-in-depth with three concentric layers:
- Input Validation Layer — Sanitizes prompts before LLM submission
- Output Sandboxing Layer — Executes generated code in isolated environments
- Policy Enforcement Layer — Validates transformations against defined security rules
Implementation: Unified LLM Client with HolySheep
The foundation of our security boundary system is a unified API client that abstracts provider complexity while enabling consistent security enforcement across all models.
Core Client Implementation
#!/usr/bin/env python3
"""
HolySheep AI Unified Client for Secure Code Refactoring
Supports: GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, DeepSeek V3.2
"""
import requests
import hashlib
import time
import json
from typing import Optional, Dict, Any, List
from dataclasses import dataclass, field
from enum import Enum
import re
class ModelProvider(Enum):
OPENAI = "openai"
ANTHROPIC = "anthropic"
GEMINI = "gemini"
DEEPSEEK = "deepseek"
@dataclass
class LLMConfig:
model: str
provider: ModelProvider
max_tokens: int = 4096
temperature: float = 0.3
base_url: str = "https://api.holysheep.ai/v1"
@dataclass
class RefactorRequest:
source_code: str
file_path: str
language: str
security_policy: str = "strict"
allowed_imports: List[str] = field(default_factory=list)
blocked_patterns: List[str] = field(default_factory=list)
class HolySheepRefactorClient:
"""Secure code refactoring client with built-in boundary enforcement."""
PROVIDER_MODELS = {
ModelProvider.OPENAI: "gpt-4.1",
ModelProvider.ANTHROPIC: "claude-sonnet-4-5",
ModelProvider.GEMINI: "gemini-2.5-flash",
ModelProvider.DEEPSEEK: "deepseek-v3.2"
}
def __init__(self, api_key: str):
if not api_key or len(api_key) < 20:
raise ValueError("Invalid API key format")
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self._request_count = 0
self._last_request_time = 0
def _rate_limit(self, requests_per_second: int = 10):
"""Enforce client-side rate limiting."""
min_interval = 1.0 / requests_per_second
elapsed = time.time() - self._last_request_time
if elapsed < min_interval:
time.sleep(min_interval - elapsed)
self._last_request_time = time.time()
def _validate_source_code(self, code: str, language: str) -> bool:
"""Input validation: reject oversized or malformed input."""
if len(code) > 100_000: # 100KB limit
raise ValueError(f"Source code exceeds 100KB limit: {len(code)} bytes")
dangerous_patterns = [
r'eval\s*\(',
r'exec\s*\(',
r'__import__\s*\(',
r'compile\s*\(',
r'os\.system',
r'subprocess\.(call|run|Popen)',
]
for pattern in dangerous_patterns:
if re.search(pattern, code):
raise ValueError(f"Dangerous pattern detected: {pattern}")
return True
def _sanitize_prompt(self, request: RefactorRequest) -> str:
"""Sanitize and construct safe refactoring prompt."""
# Remove potential prompt injection vectors
sanitized_code = request.source_code.replace("\\x00", "")
sanitized_code = sanitized_code.replace("{{", "").replace("}}", "")
policy_instruction = f"""
SECURITY CONSTRAINTS:
- Language: {request.language}
- Security Level: {request.security_policy}
- Allowed imports: {', '.join(request.allowed_imports) or 'none'}
- Blocked patterns: {', '.join(request.blocked_patterns) or 'none'}
- DO NOT suggest code containing: eval(), exec(), __import__(), os.system()
"""
prompt = f"""Refactor the following {request.language} code for better maintainability.
Focus on: readability, performance, and adherence to SOLID principles.
File: {request.file_path}
{policy_instruction}
SOURCE CODE:
```{request.language}
{sanitized_code}
```
Return ONLY the refactored code in a JSON envelope:
{{"refactored_code": "...", "changes_summary": [...], "security_notes": "..."}}
"""
return prompt
def refactor(
self,
request: RefactorRequest,
provider: ModelProvider = ModelProvider.DEEPSEEK
) -> Dict[str, Any]:
"""Execute secure refactoring request via HolySheep relay."""
# Step 1: Validate input
self._validate_source_code(request.source_code, request.language)
# Step 2: Rate limiting
self._rate_limit()
# Step 3: Construct sanitized prompt
prompt = self._sanitize_prompt(request)
model = self.PROVIDER_MODELS[provider]
# Step 4: Execute via HolySheep unified endpoint
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
"X-Request-ID": hashlib.sha256(f"{time.time()}{self._request_count}".encode()).hexdigest()[:16]
}
payload = {
"model": model,
"messages": [{"role": "user", "content": prompt}],
"max_tokens": request.max_tokens or self.max_tokens,
"temperature": request.temperature
}
# Using OpenAI-compatible endpoint for all providers
endpoint = f"{self.base_url}/chat/completions"
try:
response = requests.post(endpoint, headers=headers, json=payload, timeout=30)
response.raise_for_status()
result = response.json()
self._request_count += 1
# Extract and validate response
content = result["choices"][0]["message"]["content"]
return self._parse_and_validate_response(content, request)
except requests.exceptions.Timeout:
raise TimeoutError(f"HolySheep API timeout (>30s) for model {model}")
except requests.exceptions.HTTPError as e:
if e.response.status_code == 401:
raise PermissionError("Invalid HolySheep API key")
raise RuntimeError(f"HolySheep API error: {e}")
except (KeyError, IndexError) as e:
raise ValueError(f"Malformed API response: {e}")
Usage example
if __name__ == "__main__":
client = HolySheepRefactorClient("YOUR_HOLYSHEEP_API_KEY")
request = RefactorRequest(
source_code='''
def calculate_discount(price, discount):
return price - (price * discount / 100)
def process_order(order_id, items, discount=0):
total = sum(item['price'] * item['qty'] for item in items)
discounted = calculate_discount(total, discount)
return {'order_id': order_id, 'total': discounted}
''',
file_path="orders.py",
language="python",
security_policy="strict",
allowed_imports=["datetime", "json"],
blocked_patterns=["eval", "exec", "subprocess"]
)
result = client.refactor(request, provider=ModelProvider.DEEPSEEK)
print(json.dumps(result, indent=2))
Output Sandboxing: Safe Code Execution Environment
The second critical layer is output sandboxing—isolating generated code execution from production systems. I learned this the hard way when an LLM-suggested refactor included a file system operation that corrupted a staging database.
#!/usr/bin/env python3
"""
Secure Code Sandbox for AI-Generated Refactors
Implements process isolation, syscall filtering, and resource limits
"""
import subprocess
import resource
import tempfile
import os
import json
import hashlib
import ast
from typing import Dict, Any, List, Tuple
from dataclasses import dataclass
import multiprocessing as mp
@dataclass
class SandboxConfig:
max_cpu_time: int = 5 # seconds
max_memory: int = 256 * 1024 * 1024 # 256MB
max_output_size: int = 64 * 1024 # 64KB
allowed_modules: Tuple[str, ...] = ("math", "json", "datetime", "collections", "itertools")
network_access: bool = False
filesystem_access: bool = False
class SecurityPolicyViolation(Exception):
"""Raised when code violates security policy."""
pass
class CodeAnalyzer:
"""Static analysis to detect dangerous patterns before execution."""
DANGEROUS_AST_NODES = {
'Call': {
'eval', 'exec', 'compile', 'open', 'input',
'__import__', 'getattr', 'setattr', 'delattr',
'locals', 'globals', 'vars', 'memoryview'
},
'Attribute': {
'system', 'popen', 'spawn', 'fork',
'read', 'write' # for file objects
}
}
DANGEROUS_PATTERNS = [
(r'__import__\s*\(', 'Dynamic import detected'),
(r'eval\s*\(', 'eval() usage detected'),
(r'exec\s*\(', 'exec() usage detected'),
(r'open\s*\([^)]*[\'"]?[rwa][^\'\"]*[\'"]?', 'File operation detected'),
(r'subprocess\.', 'Subprocess module usage detected'),
(r'os\.system', 'OS command execution detected'),
(r'socket\.', 'Network socket detected'),
(r'requests\.', 'HTTP requests detected'),
(r'ctypes\.', 'C FFI detected'),
]
@classmethod
def analyze(cls, code: str, language: str = "python") -> List[Dict[str, Any]]:
"""Perform static analysis on generated code."""
violations = []
if language == "python":
try:
tree = ast.parse(code)
violations.extend(cls._analyze_ast(tree, code))
except SyntaxError as e:
violations.append({
"severity": "error",
"type": "syntax_error",
"message": str(e)
})
# Pattern-based detection for all languages
for pattern, message in cls.DANGEROUS_PATTERNS:
import re
matches = list(re.finditer(pattern, code))
for match in matches:
violations.append({
"severity": "critical",
"type": "dangerous_pattern",
"message": message,
"line": code[:match.start()].count('\n') + 1,
"match": match.group()
})
return violations
@classmethod
def _analyze_ast(cls, tree: ast.AST, code: str) -> List[Dict[str, Any]]:
violations = []
class SecurityVisitor(ast.NodeVisitor):
def visit_Call(self, node):
if isinstance(node.func, ast.Name):
if node.func.id in cls.DANGEROUS_AST_NODES['Call']:
violations.append({
"severity": "critical",
"type": "dangerous_call",
"message": f"Dangerous function: {node.func.id}",
"line": node.lineno
})
elif isinstance(node.func, ast.Attribute):
if node.func.attr in cls.DANGEROUS_AST_NODES['Attribute']:
violations.append({
"severity": "critical",
"type": "dangerous_attribute",
"message": f"Dangerous attribute access: {node.func.attr}",
"line": node.lineno
})
self.generic_visit(node)
SecurityVisitor().visit(tree)
return violations
class SecureSandbox:
"""Isolated execution environment for AI-generated code."""
def __init__(self, config: SandboxConfig = None):
self.config = config or SandboxConfig()
def _set_resource_limits(self):
"""Configure OS-level resource limits."""
resource.setrlimit(resource.RLIMIT_CPU,
(self.config.max_cpu_time, self.config.max_cpu_time))
resource.setrlimit(resource.RLIMIT_AS,
(self.config.max_memory, self.config.max_memory))
resource.setrlimit(resource.RLIMIT_FSIZE,
(1024*1024, 1024*1024)) # 1MB file size limit
def _create_isolated_namespace(self) -> Dict[str, Any]:
"""Create restricted globals for code execution."""
import builtins
import math
import json
import datetime
import collections
import itertools
allowed_builtins = {
name: getattr(builtins, name)
for name in ['len', 'range', 'int', 'float', 'str', 'bool',
'list', 'dict', 'tuple', 'set', 'print', 'sum',
'min', 'max', 'abs', 'round', 'sorted', 'reversed',
'enumerate', 'zip', 'map', 'filter', 'isinstance',
'issubclass', 'hasattr', 'getattr', 'type', 'None',
'True', 'False', 'and', 'or', 'not', 'in', 'for',
'if', 'elif', 'else', 'return', 'def', 'class']
if hasattr(builtins, name)
}
return {
'__builtins__': allowed_builtins,
'math': math,
'json': json,
'datetime': datetime,
'collections': collections,
'itertools': itertools,
'__name__': '__sandbox__'
}
def execute(self, code: str, test_input: Dict[str, Any] = None) -> Dict[str, Any]:
"""
Execute code in isolated sandbox with full boundary enforcement.
Returns execution result with security audit trail.
"""
# Step 1: Pre-execution static analysis
violations = CodeAnalyzer.analyze(code)
if any(v['severity'] == 'critical' for v in violations):
raise SecurityPolicyViolation(
f"Code blocked: {[v['message'] for v in violations if v['severity'] == 'critical']}"
)
# Step 2: Write to temp file with restricted permissions
fd, filepath = tempfile.mkstemp(suffix='.py', prefix='refactor_')
try:
os.write(fd, code.encode('utf-8'))
os.fchmod(fd, 0o600) # Owner read/write only
os.close(fd)
# Step 3: Execute with restricted permissions
result = self._run_isolated(filepath, test_input)
result['security_audit'] = {
'static_analysis': violations,
'resource_limits': {
'max_cpu_time': self.config.max_cpu_time,
'max_memory_mb': self.config.max_memory // (1024*1024)
},
'code_hash': hashlib.sha256(code.encode()).hexdigest()
}
return result
finally:
if os.path.exists(filepath):
os.unlink(filepath)
def _run_isolated(self, filepath: str, test_input: Dict) -> Dict[str, Any]:
"""Run code in child process with strict resource limits."""
isolation_script = f'''
import sys
import resource
import json
Apply resource limits
resource.setrlimit(resource.RLIMIT_CPU, ({self.config.max_cpu_time}, {self.config.max_cpu_time}))
resource.setrlimit(resource.RLIMIT_AS, ({self.config.max_memory}, {self.config.max_memory}))
Restricted globals
_globals = {self._create_isolated_namespace()}
_input = {test_input or {}}
Execute with timeout
sys.path.insert(0, '/tmp')
exec(open('{filepath}').read(), _globals)
'''
# Create minimal execution environment
exec_env = os.environ.copy()
exec_env['PYTHONDONTWRITEBYTECODE'] = '1'
exec_env['PYTHONPATH'] = ''
try:
result = subprocess.run(
['python3', '-c', isolation_script],
capture_output=True,
text=True,
timeout=self.config.max_cpu_time + 2,
env=exec_env,
cwd='/tmp'
)
return {
'success': result.returncode == 0,
'stdout': result.stdout[:self.config.max_output_size],
'stderr': result.stderr[:self.config.max_output_size],
'exit_code': result.returncode
}
except subprocess.TimeoutExpired:
return {
'success': False,
'stdout': '',
'stderr': 'Execution timeout exceeded',
'exit_code': -1
}
Integration with HolySheep client
def secure_refactor_pipeline(
api_key: str,
source_code: str,
file_path: str,
language: str = "python",
model: str = "deepseek-v3.2"
) -> Dict[str, Any]:
"""
End-to-end secure refactoring pipeline:
1. Generate refactored code via HolySheep
2. Static analysis on generated code
3. Sandboxed execution of tests
4. Security audit trail
"""
from holy_sheep_client import HolySheepRefactorClient, RefactorRequest, ModelProvider
# Generate
client = HolySheepRefactorClient(api_key)
request = RefactorRequest(
source_code=source_code,
file_path=file_path,
language=language
)
generation_result = client.refactor(request)
# Parse generated code
try:
refactored_code = json.loads(generation_result['choices'][0]['message']['content'])
code_to_verify = refactored_code.get('refactored_code', '')
except (json.JSONDecodeError, KeyError):
code_to_verify = generation_result['choices'][0]['message']['content']
# Verify in sandbox
sandbox = SecureSandbox()
execution_result = sandbox.execute(code_to_verify)
return {
'generation': generation_result,
'verification': execution_result,
'refactored_code': code_to_verify if execution_result['success'] else None,
'status': 'verified' if execution_result['success'] else 'verification_failed'
}
Policy Enforcement: Defining Security Rules for Your Organization
Beyond technical boundaries, your refactoring pipeline needs explicit policy definitions. I recommend creating a domain-specific policy language that security teams can audit without understanding ML internals.
#!/usr/bin/env python3
"""
Security Policy Definition and Enforcement Engine
Define org-wide rules as versioned configuration
"""
import json
import hashlib
import yaml
from typing import Dict, List, Any, Optional
from dataclasses import dataclass, field, asdict
from datetime import datetime
from enum import Enum
import re
class PolicySeverity(Enum):
BLOCK = "block"
WARN = "warn"
LOG = "log"
class PolicyType(Enum):
IMPORT_RESTRICTION = "import_restriction"
PATTERN_BLACKLIST = "pattern_blacklist"
PATTERN_WHITELIST = "pattern_whitelist"
RESOURCE_LIMIT = "resource_limit"
CODE_COMPLEXITY = "code_complexity"
@dataclass
class PolicyRule:
id: str
name: str
policy_type: PolicyType
severity: PolicySeverity
description: str
config: Dict[str, Any]
enabled: bool = True
applies_to_languages: List[str] = field(default_factory=lambda: ["*"])
tags: List[str] = field(default_factory=list)
def to_dict(self) -> Dict:
return {
**asdict(self),
'policy_type': self.policy_type.value,
'severity': self.severity.value
}
@dataclass
class SecurityPolicy:
version: str
name: str
description: str
rules: List[PolicyRule]
created_at: str
created_by: str
metadata: Dict[str, Any] = field(default_factory=dict)
def to_dict(self) -> Dict:
return {
'version': self.version,
'name': self.name,
'description': self.description,
'created_at': self.created_at,
'created_by': self.created_by,
'rules': [r.to_dict() for r in self.rules],
'metadata': self.metadata
}
@property
def checksum(self) -> str:
"""Generate deterministic hash for policy versioning."""
policy_json = json.dumps(self.to_dict(), sort_keys=True)
return hashlib.sha256(policy_json.encode()).hexdigest()[:16]
class PolicyEngine:
"""Enforce security policies on code transformations."""
def __init__(self, policy: SecurityPolicy):
self.policy = policy
self._rule_index = {r.id: r for r in policy.rules}
def evaluate(self, code: str, language: str) -> Dict[str, Any]:
"""Evaluate all applicable policies against code."""
violations = []
warnings = []
logs = []
for rule in self.policy.rules:
if not rule.enabled:
continue
if not self._applies_to_language(rule, language):
continue
result = self._evaluate_rule(rule, code)
if result:
entry = {
'rule_id': rule.id,
'rule_name': rule.name,
'severity': rule.severity.value,
'details': result,
'timestamp': datetime.utcnow().isoformat()
}
if rule.severity == PolicySeverity.BLOCK:
violations.append(entry)
elif rule.severity == PolicySeverity.WARN:
warnings.append(entry)
else:
logs.append(entry)
return {
'compliant': len(violations) == 0,
'violations': violations,
'warnings': warnings,
'logs': logs,
'policy_version': self.policy.version,
'policy_checksum': self.policy.checksum
}
def _applies_to_language(self, rule: PolicyRule, language: str) -> bool:
return "*" in rule.applies_to_languages or language in rule.applies_to_languages
def _evaluate_rule(self, rule: PolicyRule, code: str) -> Optional[Dict]:
evaluators = {
PolicyType.IMPORT_RESTRICTION: self._check_imports,
PolicyType.PATTERN_BLACKLIST: self._check_blacklist,
PolicyType.PATTERN_WHITELIST: self._check_whitelist,
PolicyType.RESOURCE_LIMIT: self._check_resources,
PolicyType.CODE_COMPLEXITY: self._check_complexity
}
evaluator = evaluators.get(rule.policy_type)
if evaluator:
return evaluator(rule, code)
return None
def _check_imports(self, rule: PolicyRule, code: str) -> Optional[Dict]:
"""Verify import statements against policy."""
allowed = rule.config.get('allowed_imports', [])
blocked = rule.config.get('blocked_imports', [])
import_pattern = r'(?:from\s+(\w+)|import\s+(\w+))'
imports = re.findall(import_pattern, code)
flat_imports = [imp for pair in imports for imp in pair if imp]
violations = []
for imp in flat_imports:
if blocked and imp in blocked:
violations.append(f"Blocked import: {imp}")
if allowed and imp not in allowed and imp not in ['typing', 'dataclasses']:
violations.append(f"Unauthorized import: {imp}")
return {'imports_found': flat_imports, 'violations': violations} if violations else None
def _check_blacklist(self, rule: PolicyRule, code: str) -> Optional[Dict]:
"""Check code against blacklist patterns."""
patterns = rule.config.get('patterns', [])
violations = []
for pattern in patterns:
matches = list(re.finditer(pattern, code, re.MULTILINE))
if matches:
violations.append({
'pattern': pattern,
'matches': [{'line': m.start()} for m in matches]
})
return {'violations': violations} if violations else None
def _check_whitelist(self, rule: PolicyRule, code: str) -> Optional[Dict]:
"""Ensure code only contains whitelisted constructs."""
required = rule.config.get('required_patterns', [])
missing = []
for pattern in required:
if not re.search(pattern, code):
missing.append(pattern)
return {'missing_patterns': missing} if missing else None
def _check_resources(self, rule: PolicyRule, code: str) -> Optional[Dict]:
"""Estimate resource requirements."""
issues = []
if rule.config.get('max_loops'):
loop_count = len(re.findall(r'\b(for|while)\b', code))
if loop_count > rule.config['max_loops']:
issues.append(f"Loop count ({loop_count}) exceeds limit")
if rule.config.get('max_function_length'):
functions = re.findall(r'def\s+\w+\([^)]*\):', code)
for func in functions:
if len(code.split(func)[1].split('def ')[0]) > rule.config['max_function_length']:
issues.append("Function exceeds max length")
return {'issues': issues} if issues else None
def _check_complexity(self, rule: PolicyRule, code: str) -> Optional[Dict]:
"""Estimate cyclomatic complexity."""
try:
import ast
tree = ast.parse(code)
def count_branches(node):
count = 1
for child in ast.walk(node):
if isinstance(child, (ast.If, ast.While, ast.For,
ast.ExceptHandler, ast.With)):
count += 1
elif isinstance(child, ast.BoolOp):
count += len(child.values) - 1
return count
functions = [n for n in ast.walk(tree) if isinstance(n, ast.FunctionDef)]
complexities = [(f.name, count_branches(f)) for f in functions]
max_allowed = rule.config.get('max_complexity', 10)
violations = [(name, c) for name, c in complexities if c > max_allowed]
return {'functions': complexities, 'violations': violations} if violations else None
except:
return None
Default policy factory
def create_strict_python_policy() -> SecurityPolicy:
"""Create enterprise-grade strict policy for Python refactoring."""
rules = [
PolicyRule(
id="PY-001",
name="Block Dangerous Built-ins",
policy_type=PolicyType.PATTERN_BLACKLIST,
severity=PolicySeverity.BLOCK,
description="Prevent use of eval, exec, and similar functions",
config={'patterns': [r'\beval\s*\(', r'\bexec\s*\(', r'\bcompile\s*\(']},
tags=["security-critical", "owasp"]
),
PolicyRule(
id="PY-002",
name="Allowed Standard Library",
policy_type=PolicyType.IMPORT_RESTRICTION,
severity=PolicySeverity.BLOCK,
description="Restrict imports to safe subset of standard library",
config={
'allowed_imports': [
'math', 'json', 'datetime', 'collections', 'itertools',
'functools', 'operator', 'string', 're', 'typing',
'dataclasses', 'enum', 'pathlib', 'abc', 'copy'
],
'blocked_imports': ['os', 'sys', 'subprocess', 'socket', 'requests']
},
tags=["security-critical"]
),
PolicyRule(
id="PY-003",
name="Complexity Limit",
policy_type=PolicyType.CODE_COMPLEXITY,
severity=PolicySeverity.WARN,
description="Warn when function complexity exceeds threshold",
config={'max_complexity': 10},
tags=["maintainability"]
),
PolicyRule(
id="PY-004",
name="File Operation Detection",
policy_type=PolicyType.PATTERN_BLACKLIST,
severity=PolicySeverity.BLOCK,
description="Block direct file system manipulation",
config={'patterns': [r'open\s*\([^)]*[\'"]?[rwa][^\'\"]*[\'"]?', r'Path\([^)]+\)\.(write|read)']},
tags=["security-critical", "data-protection"]
),
PolicyRule(
id="PY-005",
name="Network Access Detection",
policy_type=PolicyType.PATTERN_BLACKLIST,
severity=PolicySeverity.BLOCK,
description="Block network operations in refactored code",
config={'patterns': [r'requests\.', r'urllib\.', r'http\.client', r'socket\.']},
applies_to_languages=["python"],
tags=["security-critical", "data-exfiltration"]
)
]
return SecurityPolicy(
version="1.0.0",
name="Enterprise Python Refactoring Policy",
description="Strict security policy for AI-assisted Python refactoring",
rules=rules,
created_at=datetime.utcnow().isoformat(),
created_by="security-team",
metadata={'compliance': ['SOC2', 'GDPR', 'ISO27001']}
)
Usage demonstration
if __name__ == "__main__":
policy = create_strict_python_policy()
engine = PolicyEngine(policy)
test_code = '''
import os
import json
from typing import List
def process_user_data(user_id: str) -> dict:
result = eval("__import__('os').system('ls')") # BLOCKED
return json.loads('{"id": "123"}')
'''
result = engine.evaluate(test_code, "python")
print(json.dumps(result, indent=2))
# Output:
# {
# "compliant": false,
# "violations": [
# {"rule_id": "PY-001", "rule_name": "Block Dangerous Built-ins", ...},
# {"rule_id": "PY-002", "rule_name": "Allowed Standard Library", ...}
# ],
# ...
# }
Integration: End-to-End Secure Refactoring Pipeline
With the security boundary framework in place, let's integrate everything into a production-ready pipeline that you can deploy immediately using HolySheep's unified API.
Common Errors and Fixes
Based on extensive production deployment experience, here are the most frequent issues teams encounter when implementing AI-powered refactoring with security boundaries, along with their solutions.
Error 1: "Rate Limit Exceeded" on High-Volume Refactoring
Symptom: Requests fail intermittently with 429 status code during batch refactoring operations, especially when processing multiple files simultaneously.
Root Cause: HolySheep's rate limits are provider-dependent. GPT-4.1 has stricter limits (60 req/min) compared to DeepSeek V3.2 (300 req/min). Burst traffic without backoff triggers throttling.
Solution: Implement exponential backoff with jitter and respect per-model rate limits.
import time
import random
from functools import wraps
from typing import Callable, Any
class RateLimitHandler:
"""Adaptive rate limiting with exponential backoff."""
MODEL_LIMITS = {
"gpt-4.1": {"requests_per_minute": 60, "tokens_per_minute": 150000},
"claude-sonnet-4-5": {"requests_per_minute": 50, "tokens_per_minute":