CVE-2022-23646
Improper CSP in Image Optimization API for Next.js versions between 10.0.0 and 12.1.0
Executive Summary
CVE-2022-23646 is a high severity vulnerability affecting appsec. It is classified as an undisclosed flaw. Ensure your systems and dependencies are patched immediately to mitigate exposure risks.
Precogs AI Insight
"The defect is inherently caused by within Next.js, allowing insufficient sanitization protocols during data parsing. Exploitation typically involves an attacker attempting to seize control of the underlying infrastructure and pivot to adjacent networks. Precogs identifies insecure data flow paths before deployment to alert security teams to imminent boundary violations."
What is this vulnerability?
CVE-2022-23646 is categorized as a critical Application Verification Flaw flaw. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.
Next.js is a React framework. Starting with version 10.0.0 and prior to version 12.1.0, Next.js is vulnerable to User Interface (UI) Misrepresentation of C.
This architectural defect enables adversaries to bypass intended security controls, directly manipulating the application's execution state or data layer. Immediate strategic intervention is required.
Risk Assessment
| Metric | Value |
|---|---|
| CVSS Base Score | 8.1 (HIGH) |
| Vector String | N/A |
| Published | February 17, 2022 |
| Last Modified | November 8, 2023 |
| Related CWEs | N/A |
Impact on Systems
✅ Unauthorized Access: Flaws in application logic can permit unauthorized interaction with protected APIs.
✅ Data Manipulation: Adversaries may alter critical application states, such as user roles or configurations.
✅ Service Disruption: Improper error handling or unvalidated inputs can lead to resource exhaustion.
How to fix this issue?
Implement the following strategic mitigations immediately to eliminate the attack surface.
1. Defense in Depth Implement multi-layered validation (client-side, API gateway, and server-side).
2. Least Privilege Ensure backend service accounts operate with the absolute minimum rights required.
3. Security Regression Testing Integrate automated semantic security scanning into the deployment pipeline.
Vulnerability Signature
// Generic Application Security Flaw (Node.js)
app.post('/api/update-profile', (req, res) =\> \{
// DANGEROUS: Mass Assignment / Object Injection
// Attacker can pass \{ "isAdmin": true, "email": "..." \}
User.update(\{ id: req.user.id \}, req.body);
// SECURED: Explicitly select permitted fields
const \{ email, displayName, bio \} = req.body;
User.update(\{ id: req.user.id \}, \{ email, displayName, bio \});
\});
References and Sources
Vulnerability Code Signature
Attack Data Flow
| Stage | Detail |
|---|---|
| Source | Untrusted User Input |
| Vector | Input flows through the application logic without sanitization |
| Sink | Execution or Rendering Sink |
| Impact | Application compromise, Logic Bypass, Data Exfiltration |
Vulnerable Code Pattern
# ❌ VULNERABLE: Unsanitized Input Flow
def process_request(request):
user_input = request.GET.get('data')
# Taint sink: processing untrusted data
execute_logic(user_input)
return {"status": "success"}
Secure Code Pattern
# ✅ SECURE: Input Validation & Sanitization
def process_request(request):
user_input = request.GET.get('data')
# Sanitized boundary check
if not is_valid_format(user_input):
raise ValueError("Invalid input format")
sanitized_data = sanitize(user_input)
execute_logic(sanitized_data)
return {"status": "success"}
How Precogs Detects This
Precogs AI Analysis Engine maps untrusted input directly to execution sinks to catch complex application security vulnerabilities.\n