Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:HSummary
CVE-2026-39320 is a high-severity Uncontrolled Resource Consumption (CWE-400) vulnerability in Signalk Signal K Server. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Endpoint Denial of Service (T1499); ranked at the 35th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SC-5 (Denial-of-service Protection) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
Signal K Server, a Node.js application that serves as a central data hub for boat instrumentation and networking, is affected by CVE-2026-39320 in versions prior to 2.25.0. The vulnerability is an unauthenticated Regular Expression Denial of Service (ReDoS) in the WebSocket subscription handling logic. Attackers can inject unescaped regex metacharacters into the `context` parameter of a stream subscription request, triggering catastrophic backtracking in the server's regex evaluation, particularly when processing long string identifiers such as the server's self UUID. This leads to a complete denial of service with CPU utilization spiking to 100%, rendering the server unresponsive. The issue is rated CVSS 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H) and maps to CWE-400 (Uncontrolled Resource Consumption) and CWE-1333 (Inefficient Regular Expression Complexity).
Any unauthenticated remote attacker with network access to the Signal K Server can exploit this vulnerability by sending a malicious WebSocket subscription request containing specially crafted regex metacharacters in the `context` parameter. No privileges, user interaction, or prior authentication are required, making it highly accessible over the network. Successful exploitation causes the Node.js event loop to enter a resource-intensive backtracking loop, resulting in total server unresponsiveness to all API calls and socket connections, effectively denying service to legitimate users and connected boat systems.
Mitigation is available via the patch in Signal K Server version 2.25.0, which addresses the ReDoS flaw in WebSocket handling. Security advisories and GitHub references, including GHSA-7gcj-phff-2884, the fixing commit (215d81eb700d5419c3396a0fbf23f2e246dfac2d), pull request #2568, and the v2.25.0 release notes, recommend immediate upgrading to 2.25.0 or later. Practitioners should review exposed Signal K Server instances, especially on marine networks, and apply the update promptly.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-24021
Vulnerability Data
Signal K Server is a server application that runs on a central hub in a boat. Versions prior to 2.25.0 are vulnerable to an unauthenticated Regular Expression Denial of Service (ReDoS) attack within the WebSocket subscription handling logic. By injecting…
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unescaped regex metacharacters into the `context` parameter of a stream subscription, an attacker can force the server's Node.js event loop into a catastrophic backtracking loop when evaluating long string identifiers (like the server's self UUID). This results in a total Denial of Service (DoS) where the server CPU spikes to 100% and becomes completely unresponsive to further API or socket requests. Version 2.25.0 contains a fix.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover inefficient regex patterns via performance or static analysis.
SC-5 directly limits the effects of resource-exhaustion events that constitute uncontrolled consumption.
SC-6 enforces explicit allocation limits on resources, structurally preventing the weakness from occurring.
Development standards and tools can require safe regex construction and forbid known exponential patterns.
Process isolation confines resource consumption to separate domains, reducing blast radius without stopping the root flaw.
Input validation can constrain data that would otherwise trigger worst-case regex complexity.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Explicitly requires monitoring and maintaining resource capacity, directly addressing uncontrolled consumption to preserve availability.
Secure SDLC practices directly prevent inefficient regex via reviews, static analysis, and safe libraries.
Continuous monitoring of computing resources can detect resource exhaustion but does not itself enforce allocation limits.
Vulnerability identification processes can discover ReDoS issues in existing code but do not stop their introduction.
Resilience mechanisms such as avoiding single points of failure indirectly reduce impact of resource exhaustion.
Hardened configuration baselines can include resource quotas and limits that constrain consumption.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Resource-utilization monitoring and alerting on bottlenecks or overloads limits the impact of denial-of-service or resource-exhaustion attacks.
Security testing can detect and reject regex patterns with exponential worst-case complexity.
By continuously monitoring utilization, stress-testing peak loads, and maintaining documented plans to scale or throttle resources, the control directly limits an attacker’s ability to drive a system into uncontrolled resource exhaustion.
Pre-agreed severity-based prioritization and resource allocation during incident triage reduce the likelihood that an attacker-induced resource exhaustion will overwhelm the organization before corrective action is taken.
Business-continuity plans that include resource-management controls reduce the likelihood that an attacker can trigger uncontrolled resource consumption by forcing the system into a degraded or fallback state.
Defining RTOs and capacity requirements for ICT services during business-impact analysis forces organizations to provision sufficient resources and throttling mechanisms, reducing the likelihood that an attacker can induce denial-of-service through uncontrolled resource consumption.