CVE-2019-10906

Jinja2 sandbox escape via string formatting

Verified by Precogs Threat Research
Last Updated: Sep 24, 2024
Base Score
9.8CRITICAL

Executive Summary

CVE-2019-10906 is a critical 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 critical flaw stems from within Pallets Jinja, allowing the mishandling of memory allocation boundaries. Exploitation typically involves an attacker attempting to compromise the entire application stack, rendering traditional defenses ineffective. Precogs AI Analysis Engine utilizes semantic code analysis to flag these architectural defects instantly."

Exploit Probability (EPSS)
Low (3.5%)
Public POC
Available
Exploit Probability
High (84%)
Public POC
Available
Affected Assets
appsecNVD Database

What is this vulnerability?

CVE-2019-10906 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.

In Pallets Jinja before 2.10.1, str.format_map allows a sandbox escape. The sandbox is used to restrict what code can be evaluated when rendering untrus.

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

MetricValue
CVSS Base Score9.8 (CRITICAL)
Vector StringN/A
PublishedApril 10, 2019
Last ModifiedSeptember 24, 2024
Related CWEsN/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

StageDetail
SourceUntrusted User Input
VectorInput flows through the application logic without sanitization
SinkExecution or Rendering Sink
ImpactApplication 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

Is your system affected?

Precogs AI detects CVE-2019-10906 in compiled binaries, LLMs, and application layers — even without source code access.