CVE-2016-9190
Arbitrary code using "crafted image file" approach affecting Pillow
Executive Summary
CVE-2016-9190 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
"Architecturally, this flaw occurs due to within Pillow, allowing a lack of rigorous type checking mechanisms. By manipulating this weakness, a threat actor can compromise the entire application stack, rendering traditional defenses ineffective. By intercepting insecure data flows from user input directly to rendering sinks, Precogs is designed to intercept unsafe execution patterns."
What is this vulnerability?
CVE-2016-9190 is categorized as a critical Code Injection / RCE flaw. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.
Pillow before 3.3.2 allows context-dependent attackers to execute arbitrary code by using the "crafted image file" approach, related to an "Insecure Sig.
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 | July 12, 2018 |
| Last Modified | October 8, 2024 |
| Related CWEs | N/A |
Impact on Systems
✅ Remote Code Execution: Attackers achieve arbitrary command execution within the context of the application server.
✅ Privilege Escalation: Initial code execution can be exploited to pivot and elevate privileges across the network.
✅ Persistent Backdoors: Attackers can bind reverse shells, modify source files, or inject persistent access mechanisms.
How to fix this issue?
Implement the following strategic mitigations immediately to eliminate the attack surface.
1. Remove Dynamic Evaluation Completely eliminate the use of dynamic evaluation functions (eval(), exec(), system()) on untrusted input.
2. Sandboxing If dynamic execution is an absolute business requirement, isolate the execution environment in tightly constrained, non-networked sandboxes (e.g., restricted WebAssembly or isolated containers).
3. Network Segmentation Restrict outbound traffic from the application server (egress filtering) to prevent reverse shell connections.
Vulnerability Signature
// Vulnerable Node.js Execution
const exec = require('child_process').exec;
const user_domain = req.query.domain;
// VULNERABLE: Injecting user input directly into system shell commands
exec('ping -c 4 ' + user_domain, (error, stdout, stderr) =\> \{
res.send(stdout);
\});
// EXPLOIT PAYLOAD: precogs.ai ; cat /etc/passwd
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