CVE-2017-12234
Improper Input Validation in Multiple vulnerabilities in the implementation of the Common Industrial Protocol (CIP) feature in Cisco IOS 12
Executive Summary
CVE-2017-12234 is a high severity vulnerability affecting appsec. It is classified as CWE-20. This vulnerability is actively being exploited in the wild.
Precogs AI Insight
"The Common Industrial Protocol (CIP) feature incorrectly handles encapsulated CIP packets. Attackers send crafted ENIP messages to trigger integer overflows and execute arbitrary code. Precogs Binary Analysis identifies hazardous arithmetic operations in industrial protocols."
What is this vulnerability?
CVE-2017-12234 is categorized as a high Improper Input Validation flaw with a CVSS base score of 7.5. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.
Multiple vulnerabilities in the implementation of the Common Industrial Protocol (CIP) feature in Cisco IOS 12.4 through 15.6 could allow an unauthenticated, remote attacker to cause an affected device to reload, resulting in a denial of service (DoS) condition. The vulnerabilities are due to the improper parsing of crafted CIP packets destined to an affected device. An attacker could exploit these vulnerabilities by sending crafted CIP packets to be processed by an affected device. A successful exploit could allow the attacker to cause the affected device to reload, resulting in a DoS condition. Cisco Bug IDs: CSCvc43709.
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 | 7.5 (HIGH) |
| Vector String | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H |
| Published | September 29, 2017 |
| Last Modified | April 21, 2026 |
| Related CWEs | CWE-20 |
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-2017-12234
- 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
The Common Industrial Protocol (CIP) feature incorrectly handles encapsulated CIP packets. Attackers send crafted ENIP messages to trigger integer overflows and execute arbitrary code. Precogs Binary Analysis identifies hazardous arithmetic operations in industrial protocols.
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