CVE-2014-2254 in SIMATIC S7 Cpu-1211c
Summary
by MITRE
Siemens SIMATIC S7-1200 CPU PLC devices with firmware before 4.0 allow remote attackers to cause a denial of service (defect-mode transition) via crafted HTTP packets, a different vulnerability than CVE-2014-2255.
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Analysis
by VulDB Data Team • 05/08/2026
The Siemens SIMATIC S7-1200 CPU PLC devices represent critical industrial control system components used in manufacturing and automation environments where reliability and continuous operation are paramount. These programmable logic controllers operate within the industrial internet of things ecosystem and are susceptible to various cybersecurity threats that can compromise operational technology infrastructure. The vulnerability identified in CVE-2014-2254 specifically affects firmware versions prior to 4.0, indicating that this weakness has existed for several years and likely affected numerous operational environments. This vulnerability is particularly concerning because it allows remote attackers to trigger a defect-mode transition, effectively placing the PLC into a non-operational state that can disrupt production processes and potentially lead to safety hazards in industrial environments.
The technical flaw manifests through crafted HTTP packets that exploit a weakness in the device's handling of web-based management interfaces. When these malicious packets are transmitted to the PLC, they cause the device to transition into a defect mode, which represents a state where the controller cannot properly execute its programmed logic or maintain normal operational functions. This defect-mode transition constitutes a denial of service condition that can persist until manual intervention or device reboot occurs. The vulnerability operates at the application layer of the network stack, specifically targeting the HTTP server implementation within the PLC firmware. According to CWE classification, this vulnerability maps to CWE-121, which describes heap-based buffer overflow conditions, though the specific implementation appears to involve improper input validation of HTTP request parameters rather than traditional buffer overflow scenarios.
The operational impact of this vulnerability extends beyond simple service disruption to encompass potential safety risks in industrial environments where PLCs control critical machinery and processes. When a PLC enters defect mode due to this vulnerability, it can cause cascading failures throughout the industrial control system, potentially leading to equipment damage, production downtime, and in severe cases, safety incidents. The remote nature of the attack means that threat actors can exploit this weakness from outside the industrial network perimeter, potentially through unsecured internet connections or compromised network endpoints. This characteristic aligns with ATT&CK technique T1190, which describes exploitation of remote services, and represents a significant risk to operational technology environments that often lack the sophisticated network segmentation and monitoring capabilities found in traditional IT environments.
Organizations utilizing affected Siemens SIMATIC S7-1200 devices should prioritize firmware updates to version 4.0 or later, which contain patches addressing this vulnerability. Network segmentation strategies should be implemented to isolate PLCs from general network traffic, and access controls should be enforced to limit management interface access to authorized personnel only. Additionally, implementing network monitoring solutions capable of detecting anomalous HTTP traffic patterns can help identify potential exploitation attempts. The vulnerability demonstrates the importance of maintaining current firmware versions in industrial environments and highlights the need for comprehensive vulnerability management programs that address both IT and OT systems. Security professionals should also consider implementing intrusion detection systems specifically configured to monitor for HTTP-based attacks targeting industrial control systems, as these attacks often follow predictable patterns that can be detected through behavioral analysis of network traffic.