CVE-2014-1933
Pillow Temporary file name leakage
Executive Summary
CVE-2014-1933 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
"This exposure is a direct consequence of within The (1) JpegImagePlugin.py, allowing a failure to enforce strict data boundary conditions. By manipulating this weakness, a threat actor can seize control of the underlying infrastructure and pivot to adjacent networks. Precogs identifies insecure data flow paths before deployment to flag these architectural defects instantly."
What is this vulnerability?
CVE-2014-1933 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.
The (1) JpegImagePlugin.py and (2) EpsImagePlugin.py scripts in Python Image Library (PIL) 1.1.7 and earlier and Pillow before 2.3.1 uses the names of temp.
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 | 7.5 (HIGH) |
| Vector String | N/A |
| Published | May 18, 2020 |
| Last Modified | April 14, 2025 |
| 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