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How the CVE Works:
The vulnerability in LiteLLM (CVE-2025-12345) arises due to improper handling of multipart HTTP requests. Specifically, when a crafted HTTP request is sent to the server with additional characters (e.g., dashes) appended to the multipart boundary, the server enters an infinite loop of processing these characters. This results in excessive CPU and memory consumption, leading to a Denial of Service (DoS) condition. The attack is unauthenticated, meaning no user interaction or credentials are required, making it highly exploitable. The issue affects LiteLLM version v1.44.5 and potentially earlier versions if similarly configured.
DailyCVE Form:
Platform: LiteLLM
Version: v1.44.5
Vulnerability: Denial of Service (DoS)
Severity: High
Date: Mar 20, 2025
What Undercode Say:
Exploitation:
1. Crafting Malicious HTTP Request:
Use tools like `curl` or Python’s `requests` library to send a crafted HTTP request with malformed multipart boundaries.
Example:
curl -X POST http://target-server/api -H "Content-Type: multipart/form-data; boundary=-WebKitFormBoundary-" --data-binary $'WebKitFormBoundary--\r\n\r\n'
2. Resource Exhaustion:
The server will continuously process the malformed boundary, consuming CPU and memory until the service becomes unresponsive.
Protection:
1. Patch Application:
Update LiteLLM to the latest version where the vulnerability is patched.
pip install --upgrade litellm
2. Input Validation:
Implement strict input validation for multipart boundaries in HTTP requests.
Example in Python:
import re
def validate_boundary(boundary):
if not re.match(r'^[bash]+$', boundary):
raise ValueError("Invalid boundary format")
3. Rate Limiting:
Use rate-limiting mechanisms to prevent abuse.
Example with Flask:
from flask_limiter import Limiter from flask_limiter.util import get_remote_address limiter = Limiter(app, key_func=get_remote_address)
4. Web Application Firewall (WAF):
Deploy a WAF to filter out malicious requests.
Example with AWS WAF:
aws waf create-rule --name "BlockMalformedBoundary" --metric-name "MalformedBoundary" --predicates DataId=<ID>,Negated=False,Type=ByteMatch
5. Monitoring and Alerts:
Set up monitoring for unusual CPU and memory spikes.
Example with Prometheus:
- alert: HighCPUUsage
expr: 100 - (avg by(instance)(irate(node_cpu_seconds_total{mode="idle"}[bash])) 100) > 90
for: 5m
labels:
severity: critical
annotations:
summary: "High CPU usage detected"
6. Logging and Analysis:
Enable detailed logging to detect and analyze attack patterns.
Example with Python logging:
import logging
logging.basicConfig(filename='app.log', level=logging.INFO, format='%(asctime)s %(message)s')
logging.info("Received request with boundary: %s", boundary)
By following these steps, you can mitigate the risk of exploitation and ensure the security of your LiteLLM deployment.
References:
Reported By: https://github.com/advisories/GHSA-fh2c-86xm-pm2x
Extra Source Hub:
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