CVE-2026-22262 in Suricata
Summary
by MITRE • 01/27/2026
Suricata is a network IDS, IPS and NSM engine. While saving a dataset a stack buffer is used to prepare the data. Prior to versions 8.0.3 and 7.0.14, if the data in the dataset is too large, this can result in a stack overflow. Versions 8.0.3 and 7.0.14 contain a patch. As a workaround, do not use rules with datasets `save` nor `state` options.
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Analysis
by VulDB Data Team • 01/30/2026
The vulnerability identified as CVE-2026-22262 affects Suricata, a widely deployed network intrusion detection system, intrusion prevention system, and network security monitoring engine. This critical flaw resides in the dataset handling functionality of the software, specifically within the data saving mechanisms that process network traffic patterns and threat intelligence. The vulnerability manifests when Suricata attempts to store dataset information using a stack buffer that lacks proper size validation, creating a potential exploitation vector for remote attackers who can manipulate the dataset content to trigger buffer overflow conditions.
The technical implementation of this vulnerability stems from inadequate input validation within Suricata's dataset processing pipeline. When rules utilize the `save` or `state` options to store dataset information, the software allocates a fixed-size stack buffer to prepare the data for storage operations. This buffer allocation does not properly account for the potential size variations in dataset content, particularly when dealing with large datasets exceeding the allocated stack space. The flaw represents a classic stack buffer overflow vulnerability that falls under CWE-121, which specifically addresses stack-based buffer overflow conditions where insufficient bounds checking allows attackers to overwrite adjacent stack memory.
The operational impact of this vulnerability extends beyond simple denial of service scenarios, as it presents a potential path for remote code execution within the Suricata environment. Attackers who can control or influence the dataset content through malicious network traffic patterns or crafted rules could potentially exploit this buffer overflow to execute arbitrary code on systems running vulnerable versions of Suricata. This risk is particularly concerning given Suricata's deployment in critical network security infrastructure where unauthorized access could compromise entire network monitoring capabilities and potentially provide attackers with persistent access to network traffic flows.
Organizations utilizing Suricata for network security monitoring face significant operational risks from this vulnerability, as the attack surface includes any system where dataset saving functionality is enabled through rules employing the `save` or `state` options. The vulnerability affects both major release lines, with versions prior to 8.0.3 and 7.0.14 being particularly susceptible. Security teams must conduct immediate assessments of their Suricata deployments to identify systems running vulnerable versions and implement the recommended mitigation strategies. The patch released in versions 8.0.3 and 7.0.14 addresses the core buffer management issue by implementing proper size validation and dynamic buffer allocation mechanisms that prevent the overflow conditions.
The recommended mitigation approach involves upgrading to the patched versions of Suricata, which incorporate proper bounds checking and memory management practices that align with industry best practices for preventing buffer overflow vulnerabilities. Organizations unable to perform immediate upgrades should disable dataset saving functionality by avoiding rules that utilize the `save` and `state` options, effectively eliminating the attack vector while maintaining core network monitoring capabilities. This workaround strategy follows established security principles for vulnerability management and aligns with ATT&CK framework techniques related to defensive measures against memory corruption vulnerabilities. The vulnerability also highlights the importance of proper input validation and memory management in security software, particularly in systems processing potentially malicious network data that could be used to exploit buffer overflow conditions.