CVE-2019-1069
Improper Link Resolution in An elevation of privilege vulnerability exists in the way the Task Scheduler Service validates certain file operations
Executive Summary
CVE-2019-1069 is a high severity vulnerability affecting appsec. It is classified as CWE-59. This vulnerability is actively being exploited in the wild.
Precogs AI Insight
"The Windows Task Scheduler Service contains an elevation of privilege vulnerability due to improper validation of certain file operations involving symbolic links. A local adversary can manipulate tasks to overwrite sensitive system files, gaining elevated permissions on the compromised host. Precogs AI Analysis Engine intercepts unsafe parsing logic that handles privileged file operations."
What is this vulnerability?
CVE-2019-1069 is categorized as a high Improper Link Resolution 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.
An elevation of privilege vulnerability exists in the way the Task Scheduler Service validates certain file operations. An attacker who successfully exploited the vulnerability could gain elevated privileges on a victim system. To exploit the vulnerability, an attacker would require unprivileged code execution on a victim system. The security update addresses the vulnerability by correctly validating file operations.
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:L/UI:N/S:U/C:H/I:H/A:H |
| Published | June 12, 2019 |
| Last Modified | October 29, 2025 |
| Related CWEs | CWE-59, CWE-59 |
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-2019-1069
- Apply Vendor Patches Immediately: This vulnerability is listed in CISA's Known Exploited Vulnerabilities catalog. Apply updates per vendor instructions.
- 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