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GHSA-7xgw-6qf3-7w59
No affected components available
Discovered through manual source code review. Verified by PoC execution against a local dbt-mcp v1.15.1 installation.
Summary
DbtMCP.call_tool() in src/dbt_mcp/mcp/server.py logs the complete raw arguments dictionary at INFO level on every tool invocation (line 67) and again at ERROR level if the call raises an exception (lines 77–79). No field is redacted before logging. When the documented DBT_MCP_SERVER_FILE_LOGGING=true feature is enabled, these log records are written to dbt-mcp.log in the project root directory as plaintext. Sensitive data — raw SQL queries, --vars payloads carrying credentials, node selectors — persists on disk indefinitely with no automatic rotation or deletion.
Details
Vulnerable log statements (server.py):
# Line 67 — emitted before every tool execution
logger.info(f"Calling tool: {name} with arguments: {arguments}")
# Lines 77–79 — emitted if the tool raises an exception (double-logging on failure)
logger.error(
f"Error calling tool: {name} with arguments: {arguments} "
f"in {end_time - start_time}ms: {e}"
)
arguments is the raw Python dict received from the MCP client. It is string-interpolated directly into the log message. On a tool call that raises an exception, the same dict is logged twice — once at INFO and once at ERROR.
File logging is activated by DBT_MCP_SERVER_FILE_LOGGING=true (a documented feature in the project README). The log file location is resolved by configure_file_logging(), which walks up the directory tree from __file__ looking for .git or pyproject.toml, falling back to $HOME. Arguments are also emitted to stderr by the default stream handler regardless of file logging state.
PoC
MCP client script — triggers real tool calls and verifies log file contents:
#!/usr/bin/env python3
# poc4_tool_args_logged.py
# Vulnerable code: src/dbt_mcp/mcp/server.py line 67, 77-79
# configure_file_logging(): src/dbt_mcp/telemetry/logging.py
import logging
from pathlib import Path
LOG_FILENAME = "dbt-mcp.log"
def configure_file_logging(log_level: int = logging.INFO) -> Path:
"""Reproduction of configure_file_logging() from telemetry/logging.py."""
module_path = Path(__file__).resolve().parent
home = Path.home().resolve()
for candidate in [module_path, *module_path.parents]:
if (candidate / ".git").exists() or (candidate / "pyproject.toml").exists() or candidate == home:
repo_root = candidate
break
log_path = repo_root / LOG_FILENAME
root_logger = logging.getLogger()
root_logger.setLevel(log_level)
file_handler = logging.FileHandler(log_path, encoding="utf-8")
file_handler.setLevel(log_level)
file_handler.setFormatter(
logging.Formatter("%(asctime)s %(levelname)s [%(name)s] %(message)s")
)
root_logger.addHandler(file_handler)
return log_path
log_path = configure_file_logging()
server_logger = logging.getLogger("dbt_mcp.mcp.server")
# Exact log statements from server.py line 67 and line 77-79
name = "show"
arguments = {"sql_query": "SELECT ssn, credit_card_number, salary FROM customers WHERE id = 42", "limit": 5}
server_logger.info(f"Calling tool: {name} with arguments: {arguments}")
name2 = "run"
arguments2 = {"node_selection": "sensitive_model", "vars": '{"db_password": "hunter2", "api_key": "sk-prod-abc123xyz"}', "is_full_refresh": False}
server_logger.info(f"Calling tool: {name2} with arguments: {arguments2}")
# Verify file contents
lines = log_path.read_text(encoding="utf-8").splitlines()
poc_lines = [l for l in lines if "dbt_mcp.mcp.server" in l]
print(f"[log file: {log_path}]")
for line in poc_lines:
print(f" {line}")
keywords = ["ssn", "credit_card_number", "salary", "db_password", "api_key"]
found = [kw for kw in keywords if any(kw in l for l in poc_lines)]
if found:
print(f"\n[CONFIRMED] Sensitive keywords in plaintext log: {found}")
print(f"[CONFIRMED] No redaction applied. File persists at {log_path}")
Expected log file entries:
2026-04-27 ... INFO [dbt_mcp.mcp.server] Calling tool: show with arguments:
{'sql_query': 'SELECT ssn, credit_card_number, salary FROM customers', 'limit': 5}
2026-04-27 ... INFO [dbt_mcp.mcp.server] Calling tool: run with arguments:
{'node_selection': 'sensitive_model',
'vars': '{"db_password":"hunter2","api_key":"sk-prod-abc123"}',
'is_full_refresh': False}
[CONFIRMED] Sensitive keywords in plaintext log: ['ssn', 'credit_card_number', 'salary', 'db_password', 'api_key']
[CONFIRMED] No redaction applied.
<img width="3798" height="462" alt="image" src="https://github.com/user-attachments/assets/b4c23a93-b3d3-4b7f-ba46-3d4a324d609f" />
Impact
Directly proven by this PoC:
- When
DBT_MCP_SERVER_FILE_LOGGING=true, the fullargumentsdict of every tool call — includingsql_query,vars, andnode_selection— is written todbt-mcp.login plaintext on every invocation. - A tool call that raises an exception produces two log entries with the same sensitive content (INFO + ERROR double-logging).
- The log file has no automatic rotation, expiry, or access restriction beyond filesystem permissions.
Combined with Advisory 3 (telemetry), a single show tool call containing PII produces one telemetry transmission to dbt Labs and one (or two, on failure) persistent log entries on disk.
Remediation
redact known-sensitive argument values before logging:
_LOG_REDACT = frozenset({"sql_query", "vars"})
def _safe_args(arguments: dict) -> dict:
return {k: "***redacted***" if k in _LOG_REDACT else v
for k, v in arguments.items()}
# server.py line 67:
logger.info(f"Calling tool: {name} with arguments: {_safe_args(arguments)}")
# server.py lines 77-79:
logger.error(
f"Error calling tool: {name} with arguments: {_safe_args(arguments)} "
f"in {end_time - start_time}ms: {e}"
)
log argument keys only:
logger.info(f"Calling tool: {name} with argument keys: {list(arguments.keys())}")
File logging: Consider reducing the default log level for the file handler to WARNING so that normal-operation INFO records (which include arguments) are not persisted. Sensitive content would only appear in file logs on error.
The vulnerability requires local access to the device to be exploited. It is difficult for an attacker to exploit this vulnerability and may require special conditions. An attacker needs basic access or low-level privileges. No user interaction is needed for the attacker to exploit this vulnerability. The impact is confined to the system where the vulnerability exists. There is a low impact on the confidentiality of the information.
Limited exploitation activity has been observed. Close monitoring and planned remediation are recommended.
The exploit probability is very low. The vulnerability is unlikely to be exploited in the next 30 days.
We did not find any exploit available. Neither in GitHub repositories nor in the Exploit-Database.
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