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https://github.com/FuzzingLabs/fuzzforge_ai.git
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Initial commit
This commit is contained in:
Tanguy Duhamel
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"""
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API response validation and graceful degradation utilities.
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"""
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# Copyright (c) 2025 FuzzingLabs
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#
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# Licensed under the Business Source License 1.1 (BSL). See the LICENSE file
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# at the root of this repository for details.
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#
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# After the Change Date (four years from publication), this version of the
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# Licensed Work will be made available under the Apache License, Version 2.0.
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# See the LICENSE-APACHE file or http://www.apache.org/licenses/LICENSE-2.0
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#
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# Additional attribution and requirements are provided in the NOTICE file.
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import logging
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from typing import Any, Dict, List, Optional, Union
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from pydantic import BaseModel, ValidationError as PydanticValidationError
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from .exceptions import ValidationError, APIConnectionError
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logger = logging.getLogger(__name__)
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class WorkflowMetadata(BaseModel):
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"""Expected workflow metadata structure"""
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name: str
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version: str
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author: Optional[str] = None
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description: Optional[str] = None
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parameters: Dict[str, Any] = {}
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supported_volume_modes: List[str] = ["ro", "rw"]
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class RunStatus(BaseModel):
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"""Expected run status structure"""
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run_id: str
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workflow: str
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status: str
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created_at: str
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updated_at: str
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@property
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def is_completed(self) -> bool:
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"""Check if run is in a completed state"""
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return self.status.lower() in ["completed", "success", "finished"]
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@property
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def is_running(self) -> bool:
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"""Check if run is currently running"""
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return self.status.lower() in ["running", "in_progress", "active"]
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@property
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def is_failed(self) -> bool:
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"""Check if run has failed"""
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return self.status.lower() in ["failed", "error", "cancelled"]
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class FindingsResponse(BaseModel):
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"""Expected findings response structure"""
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run_id: str
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sarif: Dict[str, Any]
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total_issues: Optional[int] = None
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def model_post_init(self, __context: Any) -> None:
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"""Validate SARIF structure after initialization"""
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if not self.sarif.get("runs"):
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logger.warning(f"SARIF data for run {self.run_id} missing 'runs' section")
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elif not isinstance(self.sarif["runs"], list):
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logger.warning(f"SARIF 'runs' section is not a list for run {self.run_id}")
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def validate_api_response(response_data: Any, expected_model: type[BaseModel],
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operation: str = "API operation") -> BaseModel:
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"""
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Validate API response against expected Pydantic model.
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Args:
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response_data: Raw response data from API
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expected_model: Pydantic model class to validate against
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operation: Description of the operation for error messages
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Returns:
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Validated model instance
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Raises:
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ValidationError: If validation fails
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"""
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try:
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return expected_model.model_validate(response_data)
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except PydanticValidationError as e:
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logger.error(f"API response validation failed for {operation}: {e}")
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raise ValidationError(
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f"API response for {operation}",
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str(response_data)[:200] + "..." if len(str(response_data)) > 200 else str(response_data),
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f"valid {expected_model.__name__} format"
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) from e
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except Exception as e:
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logger.error(f"Unexpected error validating API response for {operation}: {e}")
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raise ValidationError(
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f"API response for {operation}",
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"invalid data",
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f"valid {expected_model.__name__} format"
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) from e
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def validate_sarif_structure(sarif_data: Dict[str, Any]) -> Dict[str, str]:
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"""
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Validate basic SARIF structure and return validation issues.
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Args:
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sarif_data: SARIF data dictionary
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Returns:
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Dictionary of validation issues found
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"""
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issues = {}
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# Check basic SARIF structure
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if not isinstance(sarif_data, dict):
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issues["structure"] = "SARIF data is not a dictionary"
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return issues
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if "runs" not in sarif_data:
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issues["runs"] = "Missing 'runs' section in SARIF data"
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elif not isinstance(sarif_data["runs"], list):
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issues["runs_type"] = "'runs' section is not a list"
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elif len(sarif_data["runs"]) == 0:
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issues["runs_empty"] = "'runs' section is empty"
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else:
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# Check first run structure
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run = sarif_data["runs"][0]
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if not isinstance(run, dict):
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issues["run_structure"] = "First run is not a dictionary"
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else:
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if "results" not in run:
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issues["results"] = "Missing 'results' section in run"
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elif not isinstance(run["results"], list):
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issues["results_type"] = "'results' section is not a list"
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if "tool" not in run:
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issues["tool"] = "Missing 'tool' section in run"
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elif not isinstance(run["tool"], dict):
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issues["tool_type"] = "'tool' section is not a dictionary"
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return issues
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def safe_extract_sarif_summary(sarif_data: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Safely extract summary information from SARIF data with fallbacks.
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Args:
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sarif_data: SARIF data dictionary
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Returns:
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Summary dictionary with safe defaults
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"""
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summary = {
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"total_issues": 0,
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"by_severity": {},
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"by_rule": {},
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"tools": [],
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"validation_issues": []
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}
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# Validate structure first
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validation_issues = validate_sarif_structure(sarif_data)
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if validation_issues:
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summary["validation_issues"] = list(validation_issues.values())
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logger.warning(f"SARIF validation issues: {validation_issues}")
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try:
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runs = sarif_data.get("runs", [])
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if not runs:
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return summary
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run = runs[0]
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results = run.get("results", [])
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summary["total_issues"] = len(results)
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# Count by severity/level
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for result in results:
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try:
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level = result.get("level", "note")
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rule_id = result.get("ruleId", "unknown")
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summary["by_severity"][level] = summary["by_severity"].get(level, 0) + 1
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summary["by_rule"][rule_id] = summary["by_rule"].get(rule_id, 0) + 1
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except Exception as e:
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logger.warning(f"Failed to process result: {e}")
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continue
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# Extract tool information safely
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try:
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tool = run.get("tool", {})
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driver = tool.get("driver", {})
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if driver.get("name"):
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summary["tools"].append({
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"name": driver.get("name", "unknown"),
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"version": driver.get("version", "unknown"),
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"rules": len(driver.get("rules", []))
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})
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except Exception as e:
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logger.warning(f"Failed to extract tool information: {e}")
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except Exception as e:
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logger.error(f"Failed to extract SARIF summary: {e}")
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summary["validation_issues"].append(f"Summary extraction failed: {e}")
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return summary
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def validate_workflow_parameters(parameters: Dict[str, Any],
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workflow_schema: Dict[str, Any]) -> List[str]:
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"""
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Validate workflow parameters against schema with detailed error messages.
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Args:
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parameters: Parameters to validate
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workflow_schema: JSON schema for the workflow
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Returns:
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List of validation error messages
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"""
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errors = []
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try:
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properties = workflow_schema.get("properties", {})
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required = set(workflow_schema.get("required", []))
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# Check required parameters
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missing_required = required - set(parameters.keys())
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if missing_required:
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errors.append(f"Missing required parameters: {', '.join(missing_required)}")
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# Validate individual parameters
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for param_name, param_value in parameters.items():
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if param_name not in properties:
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errors.append(f"Unknown parameter: {param_name}")
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continue
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param_schema = properties[param_name]
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param_type = param_schema.get("type", "string")
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# Type validation
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if param_type == "integer" and not isinstance(param_value, int):
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errors.append(f"Parameter '{param_name}' must be an integer")
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elif param_type == "number" and not isinstance(param_value, (int, float)):
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errors.append(f"Parameter '{param_name}' must be a number")
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elif param_type == "boolean" and not isinstance(param_value, bool):
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errors.append(f"Parameter '{param_name}' must be a boolean")
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elif param_type == "array" and not isinstance(param_value, list):
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errors.append(f"Parameter '{param_name}' must be an array")
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# Range validation for numbers
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if param_type in ["integer", "number"] and isinstance(param_value, (int, float)):
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minimum = param_schema.get("minimum")
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maximum = param_schema.get("maximum")
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if minimum is not None and param_value < minimum:
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errors.append(f"Parameter '{param_name}' must be >= {minimum}")
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if maximum is not None and param_value > maximum:
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errors.append(f"Parameter '{param_name}' must be <= {maximum}")
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except Exception as e:
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logger.error(f"Parameter validation failed: {e}")
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errors.append(f"Parameter validation error: {e}")
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return errors
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def create_fallback_response(response_type: str, **kwargs) -> Dict[str, Any]:
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"""
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Create fallback responses when API calls fail.
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Args:
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response_type: Type of response to create
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**kwargs: Additional data for the fallback
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Returns:
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Fallback response dictionary
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"""
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fallbacks = {
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"workflow_list": {
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"workflows": [],
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"message": "Unable to fetch workflows from API"
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},
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"run_status": {
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"run_id": kwargs.get("run_id", "unknown"),
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"workflow": kwargs.get("workflow", "unknown"),
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"status": "unknown",
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"created_at": kwargs.get("created_at", "unknown"),
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"updated_at": kwargs.get("updated_at", "unknown"),
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"message": "Unable to fetch run status from API"
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},
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"findings": {
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"run_id": kwargs.get("run_id", "unknown"),
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"sarif": {
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"version": "2.1.0",
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"runs": []
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},
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"message": "Unable to fetch findings from API"
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}
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}
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fallback = fallbacks.get(response_type, {"message": f"No fallback available for {response_type}"})
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logger.info(f"Using fallback response for {response_type}: {fallback.get('message', 'Unknown fallback')}")
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return fallback
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