CVE-2012-1854
Untrusted Search Path in Untrusted search path vulnerability in VBE6
Executive Summary
CVE-2012-1854 is a high severity vulnerability affecting appsec. It is classified as CWE-426. This vulnerability is actively being exploited in the wild.
Precogs AI Insight
"Microsoft Visual Basic for Applications utilizes an insecure dynamic library loading sequence that trusts relative or unsecured directory paths. Attackers drop malicious DLLs alongside legitimate documents, forcing the application to silently execute their payload upon initialization. Precogs Binary SAST engine detects DLL hijacking patterns by analyzing structural execution dependencies."
What is this vulnerability?
CVE-2012-1854 is categorized as a high Untrusted Search Path flaw with a CVSS base score of 7.8. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.
Untrusted search path vulnerability in VBE6.dll in Microsoft Office 2003 SP3, 2007 SP2 and SP3, and 2010 Gold and SP1; Microsoft Visual Basic for Applications (VBA); and Summit Microsoft Visual Basic for Applications SDK allows local users to gain privileges via a Trojan horse DLL in the current working directory, as demonstrated by a directory that contains a .docx file, aka "Visual Basic for Applications Insecure Library Loading Vulnerability," as exploited in the wild in July 2012.
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 | 7.8 (HIGH) |
| Vector String | CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H |
| Published | July 10, 2012 |
| Last Modified | April 22, 2026 |
| Related CWEs | CWE-426 |
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-2012-1854
- Apply Vendor Patches Immediately: This vulnerability is listed in CISA's Known Exploited Vulnerabilities catalog. Apply mitigations per vendor instructions, follow applicable BOD 22-01 guidance for cloud services, or discontinue use of the product if mitigations are unavailable.
- Verify Patch Deployment: Confirm all instances are updated using Precogs continuous monitoring.
- Review Audit Logs: Investigate historical access logs for indicators of compromise related to this attack surface.
- Implement Defense-in-Depth: Deploy WAF rules, network segmentation, and endpoint detection to limit blast radius.
Defending with Precogs AI
Precogs AI Analysis Engine identifies this vulnerability class through semantic code analysis powered by Code Property Graph (CPG) technology, performing inter-procedural taint tracking to detect injection flaws, broken authentication, and insecure data flows across your entire codebase.
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.
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