CVE-2026-20053 in Cyber Vision
Summary
by MITRE • 03/04/2026
Multiple Cisco products are affected by a vulnerability in the Snort 3 VBA feature that could allow an unauthenticated, remote attacker to cause the Snort 3 Detection Engine to crash.
This vulnerability is due to improper range checking when decompressing VBA data, which is user controlled. An attacker could exploit this vulnerability by sending crafted VBA data to the Snort 3 Detection Engine on the targeted device. A successful exploit could allow the attacker to cause an overflow of heap data, which could cause a DoS condition.
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Analysis
by VulDB Data Team • 05/02/2026
The vulnerability identified as CVE-2026-20053 represents a critical weakness in Cisco products that incorporate the Snort 3 Detection Engine with VBA (Visual Basic for Applications) feature functionality. This flaw exists within the processing logic responsible for decompressing user-controlled VBA data, creating a pathway for remote attackers to disrupt system operations without requiring authentication credentials. The vulnerability specifically targets the heap memory management mechanisms within the Snort 3 engine, where improper range validation during decompression operations creates opportunities for malicious input to trigger system instability. The affected Cisco products likely include network security appliances, intrusion detection systems, and other network monitoring devices that utilize Snort 3 as their core detection framework. This vulnerability demonstrates a fundamental flaw in input validation and memory management practices that could be exploited across multiple network security platforms.
The technical root cause of this vulnerability stems from inadequate bounds checking during the decompression process of VBA data structures. When the Snort 3 engine receives user-controlled VBA content, it performs decompression operations without sufficient validation of data ranges or memory boundaries. This deficiency allows an attacker to craft malicious VBA payloads that, when processed, exceed allocated heap memory boundaries and cause memory corruption. The vulnerability manifests as a heap-based buffer overflow condition that can be triggered through carefully constructed input data that manipulates the decompression algorithm's memory allocation behavior. This type of vulnerability aligns with CWE-121, which describes heap-based buffer overflow conditions, and represents a classic example of improper input validation leading to memory corruption. The attack vector requires no authentication and can be executed remotely, making it particularly dangerous in network security contexts where systems are expected to maintain continuous availability.
The operational impact of this vulnerability extends beyond simple denial-of-service conditions to potentially compromise network security monitoring capabilities. When exploited successfully, the vulnerability causes the Snort 3 Detection Engine to crash, effectively removing the device from network security operations and creating potential blind spots in network monitoring. This disruption can occur at critical moments when network traffic analysis and intrusion detection are most needed, potentially allowing malicious activities to go undetected while the system recovers from the crash. Organizations relying on Cisco network security appliances for traffic monitoring and threat detection face significant operational risks, as the crash condition could be exploited repeatedly to maintain persistent disruption of security services. The vulnerability's impact is particularly severe in environments where network security systems are expected to maintain continuous uptime and where security monitoring is critical for incident response and threat mitigation.
Mitigation strategies for CVE-2026-20053 should focus on immediate patch deployment and network segmentation to limit potential exploitation. Cisco has released security updates addressing this vulnerability that organizations should implement immediately across all affected devices. Network administrators should also consider implementing additional monitoring for unusual VBA data processing patterns and traffic anomalies that might indicate attempted exploitation. The vulnerability's characteristics suggest that input validation improvements and enhanced memory management practices should be prioritized in the overall security posture. Organizations should also review their network security configurations to ensure that VBA processing capabilities are disabled where not required, reducing the attack surface. This vulnerability highlights the importance of secure coding practices and proper memory management in security-critical software components, aligning with ATT&CK technique T1499.004 for network disruption and T1071.004 for application layer protocols. Regular security assessments and vulnerability scanning should be conducted to identify similar weaknesses in other network security components that might be susceptible to similar memory corruption attacks.