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CVE-2026-25611 is a denial-of-service vulnerability in MongoDB Server caused by asymmetric resource consumption (CWE-405). A series of specifically crafted, unauthenticated messages can exhaust available memory and crash a MongoDB server. The vulnerability was published on February 10, 2026, and is tracked under MongoDB's internal issue SERVER-116210. It carries a CVSS v3.1 base score of 7.5 (High) and a CVSS v4.0 base score of 8.7 (High) (Red Hat CVE, Cato Networks).
The root cause is classified as CWE-405 (Asymmetric Resource Consumption / Amplification), where a small number of crafted network messages trigger disproportionately large memory allocations on the server side. Because the attack requires no authentication, privileges, or user interaction, any network-accessible MongoDB instance is a potential target. The attacker sends a series of specially crafted messages over the MongoDB wire protocol, causing the server to progressively exhaust available memory until it crashes. MongoDB's internal Jira ticket SERVER-116210 tracks the fix, and Cato CTRL published a threat research report detailing the mechanics of the vulnerability (Cato Networks, MongoDB Jira).
Successful exploitation results in a complete denial of service — the targeted MongoDB server crashes and becomes unavailable, directly impacting availability with no effect on confidentiality or integrity. Because MongoDB is commonly used as a backend data store for web applications and microservices, a crash can cascade into broader application outages. The unauthenticated nature of the attack means any internet-exposed or network-accessible MongoDB instance is at risk without any prior foothold required (Cato Networks, Red Hat CVE).
The vulnerability requires no authentication, no privileges, and no user interaction, making it trivially exploitable from the network. The EPSS score is approximately 0.038%, indicating a relatively low (but non-negligible) probability of exploitation in the wild at the time of scoring. As of the available data, there is no confirmed evidence of active in-the-wild exploitation or attribution to specific threat actors, and the CVE is not listed in the CISA KEV catalog. Tenable Nessus detection plugin 298779 is available for identifying vulnerable instances, and Emerging Threats published a ruleset update (v11187, May 2026) that includes detection coverage (Tenable, Emerging Threats).
mongod.log.mongod process termination or OOM-killer events in system logs (e.g., /var/log/syslog or dmesg showing mongod killed due to memory exhaustion); unexpected MongoDB service restarts.MongoDB has addressed this vulnerability; users should upgrade to a patched version of MongoDB Server as referenced in SERVER-116210 — consult the official MongoDB advisory for specific fixed version numbers. As an immediate workaround, restrict network access to MongoDB instances using firewall rules so that only trusted hosts can reach port 27017, significantly reducing the attack surface. Enabling MongoDB authentication and binding the service to localhost or a private network interface (rather than 0.0.0.0) are additional hardening measures that limit exposure even on unpatched systems (MongoDB Jira, Red Hat CVE).
Cato CTRL published a dedicated threat research blog post titled "New MongoDB Vulnerability Allows Instant Remote Server Takedown" shortly after disclosure, drawing significant attention to the severity of the pre-authentication attack vector (Cato Networks). Multiple security news outlets including GBHackers, CyberSecurityNews, and The Hacker News covered the vulnerability, emphasizing the risk to internet-exposed MongoDB deployments (GBHackers, CyberSecurityNews). Community discussion on Reddit's r/mongodb included a thread titled "Anyone else patching for CVE-2026-25611 this weekend?", reflecting active practitioner concern and urgency around patching. Emerging Threats also added detection rules in their May 2026 ruleset update, indicating the security community is actively building defensive coverage (Emerging Threats).
Source: This report was generated using AI
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