Severity by source
AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:L
Primary rating from GitHub Advisory · only source for this CVE.
CVSS VectorGitHub Advisory
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:L
Lifecycle Timeline
5DescriptionGitHub Advisory
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_dataviaget_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 detailsAnalysisAI
KQL injection in adx-mcp-server Python package allows authenticated attackers to execute arbitrary Kusto queries against Azure Data Explorer clusters. Three MCP tool handlers (get_table_schema, sample_table_data, get_table_details) unsafely interpolate the table_name parameter into query strings via f-strings, enabling data exfiltration from arbitrary tables, execution of management commands, and potential table drops. Vendor-released patch available (commit 0abe0ee). No public exploit identified at time of analysis, though proof-of-concept code exists in the security advisory demonstrating injection via comment-based bypass and newline-separated commands. Affects adx-mcp-server ≤ commit 48b2933.
Technical ContextAI
The vulnerability resides in a Model Context Protocol (MCP) server implementation that provides AI agents with tools to query Azure Data Explorer (Kusto) databases. MCP is an emerging protocol allowing AI assistants to interact with external data sources through defined tool interfaces. The affected package constructs Kusto Query Language (KQL) statements using Python f-string interpolation without input validation. KQL, Microsoft's query language for Azure Data Explorer, supports operator chaining via pipe (|) symbols and management commands prefixed with periods (.). The vulnerability class is CWE-943 (Improper Neutralization of Special Elements in Data Query Logic), specifically a NoSQL injection variant. The tools were designed as metadata inspection utilities but accept arbitrary user input that becomes executable code. The trust boundary issue is particularly critical because MCP clients may auto-approve 'safe' metadata tools while requiring confirmation for the execute_query tool, allowing the injection to bypass security controls that users expect to protect against arbitrary query execution.
RemediationAI
Upgrade adx-mcp-server to the patched version containing commit 0abe0ee55279e111281076393e5e966335fffd30 or later, available from the GitHub repository. The patch implements proper input validation and parameterization for table_name parameters to prevent KQL injection. Installation instructions and patched code are available at https://github.com/pab1it0/adx-mcp-server/commit/0abe0ee55279e111281076393e5e966335fffd30. Review the GitHub Security Advisory GHSA-vphc-468g-8rfp at https://github.com/pab1it0/adx-mcp-server/security/advisories/GHSA-vphc-468g-8rfp for complete remediation guidance. As an interim mitigation if immediate patching is not feasible, implement allowlisting of valid table names at the application layer before invoking MCP tools, restrict Azure Data Explorer permissions for the service account to read-only access on necessary tables only, and audit MCP tool invocation logs for suspicious table_name patterns containing pipe symbols, comment markers, or newline characters. Organizations using AI agents with this MCP server should review agent prompt engineering to minimize prompt injection risks that could trigger malicious tool calls.
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External POC / Exploit Code
Leaving vuln.today
EUVD-2026-16878
GHSA-vphc-468g-8rfp