Files
AutoGPT/autogpt_platform/backend/backend/util/json.py
Zamil Majdy e79928a815 fix(backend): prevent logging sensitive data in SafeJson fallback (#12547)
### Why / What / How

**Why:** GitHub's code scanning detected a HIGH severity security
vulnerability in `/autogpt_platform/backend/backend/util/json.py:172`.
The error handler in `sanitize_json()` was logging sensitive data
(potentially including secrets, API keys, credentials) as clear text
when serialization fails.

**What:** This PR removes the logging of actual data content from the
error handler while preserving useful debugging metadata (error type,
error message, and data type).

**How:** Removed the `"Data preview: %s"` format parameter and the
corresponding `truncate(str(data), 100)` argument from the
logger.error() call. The error handler now logs only safe metadata that
helps debugging without exposing sensitive information.

### Changes 🏗️

- **Security Fix**: Modified `sanitize_json()` function in
`backend/util/json.py`
- Removed logging of data content (`truncate(str(data), 100)`) from the
error handler
  - Retained logging of error type (`type(e).__name__`)
- Retained logging of truncated error message (`truncate(str(e), 200)`)
  - Retained logging of data type (`type(data).__name__`)
- Error handler still provides useful debugging information without
exposing secrets

### Checklist 📋

#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- [x] Verified the code passes type checking (`poetry run pyright
backend/util/json.py`)
- [x] Verified the code passes linting (`poetry run ruff check
backend/util/json.py`)
  - [x] Verified all pre-commit hooks pass
- [x] Reviewed the diff to ensure only the sensitive data logging was
removed
- [x] Confirmed that useful debugging information (error type, error
message, data type) is still logged

#### For configuration changes:
- N/A - No configuration changes required
2026-03-25 04:21:21 +00:00

205 lines
6.2 KiB
Python

import logging
import re
from typing import Any, Type, TypeVar, overload
import jsonschema
import orjson
from fastapi.encoders import jsonable_encoder as to_dict
from prisma import Json
from .truncate import truncate
from .type import type_match
logger = logging.getLogger(__name__)
# Precompiled regex to remove PostgreSQL-incompatible control characters
# Removes \u0000-\u0008, \u000B-\u000C, \u000E-\u001F, \u007F (keeps tab \u0009, newline \u000A, carriage return \u000D)
POSTGRES_CONTROL_CHARS = re.compile(r"[\x00-\x08\x0B-\x0C\x0E-\x1F\x7F]")
def dumps(
data: Any, *args: Any, indent: int | None = None, option: int = 0, **kwargs: Any
) -> str:
"""
Serialize data to JSON string with automatic conversion of Pydantic models and complex types.
This function converts the input data to a JSON-serializable format using FastAPI's
jsonable_encoder before dumping to JSON. It handles Pydantic models, complex types,
and ensures proper serialization.
Parameters
----------
data : Any
The data to serialize. Can be any type including Pydantic models, dicts, lists, etc.
*args : Any
Additional positional arguments
indent : int | None
If not None, pretty-print with indentation
option : int
orjson option flags (default: 0)
**kwargs : Any
Additional keyword arguments. Supported: default, ensure_ascii, separators, indent
Returns
-------
str
JSON string representation of the data
Examples
--------
>>> dumps({"name": "Alice", "age": 30})
'{"name": "Alice", "age": 30}'
>>> dumps(pydantic_model_instance, indent=2)
'{\n "field1": "value1",\n "field2": "value2"\n}'
"""
serializable_data = to_dict(data)
# Handle indent parameter
if indent is not None or kwargs.get("indent") is not None:
option |= orjson.OPT_INDENT_2
# orjson only accepts specific parameters, filter out stdlib json params
# ensure_ascii: orjson always produces UTF-8 (better than ASCII)
# separators: orjson uses compact separators by default
supported_orjson_params = {"default"}
orjson_kwargs = {k: v for k, v in kwargs.items() if k in supported_orjson_params}
return orjson.dumps(serializable_data, option=option, **orjson_kwargs).decode(
"utf-8"
)
T = TypeVar("T")
# Sentinel value to detect when fallback is not provided
_NO_FALLBACK = object()
@overload
def loads(
data: str | bytes, *args, target_type: Type[T], fallback: T | None = None, **kwargs
) -> T:
pass
@overload
def loads(data: str | bytes, *args, fallback: Any = None, **kwargs) -> Any:
pass
def loads(
data: str | bytes,
*args,
target_type: Type[T] | None = None,
fallback: Any = _NO_FALLBACK,
**kwargs,
) -> Any:
"""Parse JSON with optional fallback on decode errors.
Args:
data: JSON string or bytes to parse
target_type: Optional type to validate/cast result to
fallback: Value to return on JSONDecodeError. If not provided, raises.
**kwargs: Additional arguments (unused, for compatibility)
Returns:
Parsed JSON data, or fallback value if parsing fails
Raises:
orjson.JSONDecodeError: Only if fallback is not provided
Examples:
>>> loads('{"valid": "json"}')
{'valid': 'json'}
>>> loads('invalid json', fallback=None)
None
>>> loads('invalid json', fallback={})
{}
>>> loads('invalid json') # raises orjson.JSONDecodeError
"""
try:
parsed = orjson.loads(data)
except orjson.JSONDecodeError:
if fallback is not _NO_FALLBACK:
return fallback
raise
if target_type:
return type_match(parsed, target_type)
return parsed
def validate_with_jsonschema(
schema: dict[str, Any], data: dict[str, Any]
) -> str | None:
"""
Validate the data against the schema.
Returns the validation error message if the data does not match the schema.
"""
try:
jsonschema.validate(data, schema)
return None
except jsonschema.ValidationError as e:
return str(e)
def sanitize_string(value: str) -> str:
"""Remove PostgreSQL-incompatible control characters from string.
Strips \\x00-\\x08, \\x0B-\\x0C, \\x0E-\\x1F, \\x7F while keeping tab,
newline, and carriage return. Use this before inserting free-form text
into PostgreSQL text/varchar columns.
"""
return POSTGRES_CONTROL_CHARS.sub("", value)
def sanitize_json(data: Any) -> Any:
try:
# Use two-pass approach for consistent string sanitization:
# 1. First convert to basic JSON-serializable types (handles Pydantic models)
# 2. Then sanitize strings in the result
basic_result = to_dict(data)
return to_dict(basic_result, custom_encoder={str: sanitize_string})
except Exception as e:
# Log the failure and fall back to string representation
logger.error(
"SafeJson fallback to string representation due to serialization error: %s (%s). "
"Data type: %s",
type(e).__name__,
truncate(str(e), 200),
type(data).__name__,
)
# Ultimate fallback: convert to string representation and sanitize
return sanitize_string(str(data))
class SafeJson(Json):
"""
Safely serialize data and return Prisma's Json type.
Sanitizes control characters to prevent PostgreSQL 22P05 errors.
This function:
1. Converts Pydantic models to dicts (recursively using to_dict)
2. Recursively removes PostgreSQL-incompatible control characters from strings
3. Returns a Prisma Json object safe for database storage
Uses to_dict (jsonable_encoder) with a custom encoder to handle both Pydantic
conversion and control character sanitization in a two-pass approach.
Args:
data: Input data to sanitize and convert to Json
Returns:
Prisma Json object with control characters removed
Examples:
>>> SafeJson({"text": "Hello\\x00World"}) # null char removed
>>> SafeJson({"path": "C:\\\\temp"}) # backslashes preserved
>>> SafeJson({"data": "Text\\\\u0000here"}) # literal backslash-u preserved
"""
def __init__(self, data: Any):
super().__init__(sanitize_json(data))