CVE-2013-1675

What is this vulnerability?

CVE-2013-1675 is categorized as a medium CWE-665 flaw with a CVSS base score of 6.5. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

Mozilla Firefox before 21.0, Firefox ESR 17.x before 17.0.6, Thunderbird before 17.0.6, and Thunderbird ESR 17.x before 17.0.6 do not properly initialize data structures for the nsDOMSVGZoomEvent::mPreviousScale and nsDOMSVGZoomEvent::mNewScale functions, which allows remote attackers to obtain sensitive information from process memory via a crafted web site.

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

Impact on Systems

✅ Data Exfiltration: Attackers can extract sensitive data from backend databases, configuration files, or internal services.

✅ Authentication Bypass: Exploiting this flaw may allow unauthorized access to protected resources and administrative interfaces.

✅ Lateral Movement: Once initial access is gained, attackers can pivot to internal systems and escalate privileges.

How to Fix and Mitigate CVE-2013-1675

1. Apply Vendor Patches Immediately: This vulnerability is listed in CISA's Known Exploited Vulnerabilities catalog. Apply updates per vendor instructions.
2. Verify Patch Deployment: Confirm all instances are updated using Precogs continuous monitoring.
3. Review Audit Logs: Investigate historical access logs for indicators of compromise related to this attack surface.
4. Implement Defense-in-Depth: Deploy WAF rules, network segmentation, and endpoint detection to limit blast radius.

Defending with Precogs AI

An uninitialized memory vulnerability in Mozilla Firefox allows attackers to bypass ASLR. Adversaries leverage this memory leak in combination with other vulnerabilities to achieve reliable remote code execution. Precogs Binary SAST engine identifies missing variable initialization.

Use Precogs to continuously scan your codebase, binaries, APIs, and infrastructure for this vulnerability class and related attack patterns. Our AI-powered detection engine combines static analysis with threat intelligence to identify exploitable weaknesses before attackers do.

Start scanning with Precogs →

Vulnerability Code Signature

Attack Data Flow

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