# Security Scan Report - Qdrant MCP Service **Service:** Qdrant MCP **Port:** 7023 **Scan Date:** 2025-11-17 **Status:** ✅ SECURE (Minor improvements recommended) ## Executive Summary **No CRITICAL or HIGH severity vulnerabilities found.** Found **2 LOW** severity issues. ## Low Severity Issues ### 1. 🟢 LOW - Missing Input Validation on Vector Dimensions - **Location:** `main.py:234-259` (vector insert/update) - **Severity:** LOW **Issue:** ```python points = [] for point_data in request.points: point = PointStruct( id=point_data.get("id"), vector=point_data.get("vector"), # ❌ No dimension validation payload=point_data.get("payload", {}) ) ``` **Risk:** - Inserting vectors with wrong dimensions causes Qdrant errors - No validation before database operation **Recommendation:** ```python async def validate_vector_dimension( collection_name: str, vector: List[float] ) -> None: """Validate vector matches collection dimension""" info = await qdrant_client.get_collection(collection_name) expected_size = info.config.params.vectors.size if len(vector) != expected_size: raise HTTPException( status_code=400, detail=f"Vector dimension mismatch. Expected {expected_size}, got {len(vector)}" ) ``` --- ### 2. 🟢 LOW - No Payload Size Limits - **Location:** Vector and collection operations - **Severity:** LOW **Issue:** Large payloads could impact Qdrant performance **Recommendation:** ```python MAX_PAYLOAD_SIZE = 100 * 1024 # 100KB def validate_payload_size(payload: dict) -> None: """Validate payload size""" payload_str = json.dumps(payload) if len(payload_str.encode('utf-8')) > MAX_PAYLOAD_SIZE: raise HTTPException( status_code=413, detail=f"Payload too large (max {MAX_PAYLOAD_SIZE} bytes)" ) ``` --- ### 3. 🟢 LOW - No Collection Name Validation - **Location:** All collection operations - **Severity:** LOW **Issue:** Collection names not validated for special characters **Recommendation:** ```python import re def validate_collection_name(name: str) -> str: """Validate collection name format""" if not re.match(r'^[a-zA-Z0-9_-]+$', name): raise HTTPException( status_code=400, detail="Collection name must contain only alphanumeric, _, -" ) if len(name) > 64: raise HTTPException( status_code=400, detail="Collection name too long (max 64 characters)" ) return name ``` --- ### 4. 🟢 LOW - Filter Injection Potential - **Location:** `main.py:356-363` (search filter) - **Severity:** LOW **Issue:** ```python if request.filter: conditions = [] for field, value in request.filter.items(): conditions.append(FieldCondition(key=field, match=MatchValue(value=value))) ``` **Risk:** Limited - Qdrant handles filter validation **Recommendation:** Add field name validation ## Security Strengths ✅ **Excellent Practices:** 1. ✅ Proper async/await usage throughout 2. ✅ Connection lifecycle management with lifespan 3. ✅ Type hints and Pydantic validation 4. ✅ Proper error handling and logging 5. ✅ Health check with connection status 6. ✅ Uses Qdrant client properly 7. ✅ No direct database queries (uses official client) 8. ✅ Good separation of concerns ✅ **No Major Security Issues:** - No SQL injection (uses vector DB) - No path traversal - No command injection - No authentication bypass - Proper use of Qdrant SDK ## Recommendations ### Security Enhancements: 1. Add input validation for all user inputs 2. Add rate limiting for expensive operations (search, bulk insert) 3. Add authentication/authorization 4. Add audit logging for collection management 5. Implement quotas per user/tenant 6. Add monitoring for query performance ### Code Example - Complete Input Validation: ```python from pydantic import validator import re class VectorRequest(BaseModel): operation: str collection_name: str points: Optional[List[Dict[str, Any]]] = None point_id: Optional[Union[int, str]] = None vector: Optional[List[float]] = None payload: Optional[Dict[str, Any]] = None @validator('collection_name') def validate_collection_name(cls, v): if not re.match(r'^[a-zA-Z0-9_-]+$', v): raise ValueError('Invalid collection name format') if len(v) > 64: raise ValueError('Collection name too long') return v @validator('vector') def validate_vector(cls, v): if v: # Check for valid float values if any(not isinstance(x, (int, float)) for x in v): raise ValueError('Vector must contain only numbers') # Check for NaN, Inf if any(math.isnan(x) or math.isinf(x) for x in v): raise ValueError('Vector contains invalid values (NaN/Inf)') # Size limit if len(v) > 4096: raise ValueError('Vector dimension too large (max 4096)') return v @validator('payload') def validate_payload(cls, v): if v: payload_size = len(json.dumps(v).encode('utf-8')) if payload_size > 100 * 1024: # 100KB raise ValueError('Payload too large (max 100KB)') return v @validator('points') def validate_points(cls, v): if v and len(v) > 1000: raise ValueError('Too many points in single request (max 1000)') return v ``` ### Code Example - Rate Limiting for Expensive Operations: ```python from slowapi import Limiter from slowapi.util import get_remote_address limiter = Limiter(key_func=get_remote_address) @app.post("/tools/search") @limiter.limit("100/minute") # Limit vector searches async def search_tool(request: Request, search_req: SearchRequest): # ... implementation ``` ### Code Example - Audit Logging: ```python import logging audit_logger = logging.getLogger('audit') async def audit_log( operation: str, collection: str, user: str, details: dict ): """Log security-relevant operations""" audit_logger.info( f"operation={operation} collection={collection} user={user} details={details}" ) @app.post("/tools/collection") async def collection_tool(request: CollectionRequest): if request.operation == "delete": await audit_log( operation="collection_delete", collection=request.collection_name, user="system", # Add actual user from auth details={"timestamp": datetime.now().isoformat()} ) # ... rest of implementation ``` ### Code Example - Query Performance Monitoring: ```python import time @app.post("/tools/search") async def search_tool(request: SearchRequest): start_time = time.time() try: # ... search logic ... duration = time.time() - start_time # Log slow queries if duration > 1.0: # 1 second threshold logger.warning( f"Slow query detected: {duration:.2f}s " f"collection={request.collection_name} " f"limit={request.limit}" ) # ... return results except Exception as e: logger.error(f"Search failed after {time.time() - start_time:.2f}s: {e}") raise ``` ## Performance Considerations ### Best Practices: 1. ✅ Use batch operations for multiple inserts 2. ✅ Set appropriate search limits 3. ✅ Use score thresholds to reduce result set 4. ✅ Consider using filters to narrow search space ### Recommendations: 1. Add pagination for large result sets 2. Implement query result caching for common searches 3. Monitor collection size and implement archival 4. Add indices for frequently filtered fields ## Compliance Notes - **OWASP Top 10:** No significant violations - **Data Privacy:** Add considerations for PII in payloads - **GDPR:** Implement data deletion capabilities ## Testing Recommendations 1. Test with maximum vector dimensions 2. Test with large batch inserts 3. Test concurrent operations 4. Test with malformed vectors (NaN, Inf) 5. Load test search operations ## Next Steps 1. Add input validation for vector dimensions and payloads 2. Implement rate limiting for expensive operations 3. Add authentication and authorization 4. Add comprehensive audit logging 5. Monitor query performance and set alerts 6. Document API usage limits --- **Auditor Notes:** This is the most secure service in the audit. The implementation follows best practices and uses the Qdrant SDK properly. Only minor enhancements for input validation and monitoring are recommended. The service is production-ready with the recommended improvements.