CVE-2018-0180
CWE-399 in Multiple vulnerabilities in the Login Enhancements (Login Block) feature of Cisco IOS Software could allow an unauthenticated, remote attacker to trigger a reload of an affected system, resulting in a denial of service (DoS) condition
Executive Summary
CVE-2018-0180 is a medium severity vulnerability affecting appsec. It is classified as CWE-399. This vulnerability is actively being exploited in the wild.
Precogs AI Insight
"The Login Enhancements feature of Cisco IOS Software contains a race condition that leads to resource exhaustion when handling concurrent administrative login attempts. An unauthenticated, remote attacker can flood the device with specific login requests to trigger a reload, causing a sustained Denial of Service. Precogs AI Analysis Engine identifies race conditions and unsafe resource allocation patterns."
What is this vulnerability?
CVE-2018-0180 is categorized as a medium CWE-399 flaw with a CVSS base score of 5.9. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.
Multiple vulnerabilities in the Login Enhancements (Login Block) feature of Cisco IOS Software could allow an unauthenticated, remote attacker to trigger a reload of an affected system, resulting in a denial of service (DoS) condition. These vulnerabilities affect Cisco devices that are running Cisco IOS Software Release 15.4(2)T, 15.4(3)M, or 15.4(2)CG and later. Cisco Bug IDs: CSCuy32360, CSCuz60599.
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 | 5.9 (MEDIUM) |
| Vector String | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H |
| Published | March 28, 2018 |
| Last Modified | January 14, 2026 |
| Related CWEs | CWE-399 |
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-2018-0180
- 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