CVE-2018-0155

What is this vulnerability?

CVE-2018-0155 is categorized as a high CWE-388 flaw with a CVSS base score of 8.6. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

A vulnerability in the Bidirectional Forwarding Detection (BFD) offload implementation of Cisco Catalyst 4500 Series Switches and Cisco Catalyst 4500-X Series Switches could allow an unauthenticated, remote attacker to cause a crash of the iosd process, causing a denial of service (DoS) condition. The vulnerability is due to insufficient error handling when the BFD header in a BFD packet is incomplete. An attacker could exploit this vulnerability by sending a crafted BFD message to or across an affected switch. A successful exploit could allow the attacker to trigger a reload of the system. This vulnerability affects Catalyst 4500 Supervisor Engine 6-E (K5), Catalyst 4500 Supervisor Engine 6L-E (K10), Catalyst 4500 Supervisor Engine 7-E (K10), Catalyst 4500 Supervisor Engine 7L-E (K10), Catalyst 4500E Supervisor Engine 8-E (K10), Catalyst 4500E Supervisor Engine 8L-E (K10), Catalyst 4500E Supervisor Engine 9-E (K10), Catalyst 4500-X Series Switches (K10), Catalyst 4900M Switch (K5), Catalyst 4948E Ethernet Switch (K5). Cisco Bug IDs: CSCvc40729.

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-2018-0155

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

A race condition in the Bidirectional Forwarding Detection (BFD) offload implementation corrupts internal data structures. Attackers can flip line protocol states, isolating critical network segments. Precogs Application Security Module analyzes concurrent execution paths to identify race conditions.

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