CVE-2026-44830 (GCVE-0-2026-44830)
Vulnerability from cvelistv5 – Published: 2026-05-27 14:19 – Updated: 2026-05-27 15:33
VLAI
EPSS
VEX
Title
Empty API_TOKEN disables authentication on network-reachable HTTP/SSE transport
Summary
Nocturne Memory is a lightweight, rollbackable, and visual Long-Term Memory Server for MCP Agents. Prior to 2.4.1, when API_TOKEN is unset or empty, the BearerTokenAuthMiddleware bypasses authentication for all HTTP requests. Combined with the default 0.0.0.0 host binding and CORS allow_origins=["*"], operators following the Docker setup without explicitly setting API_TOKEN expose the full Knowledge-Graph read/write API to any LAN-reachable client. An attacker on the same network can read, write, or delete all memory entries — including system://boot and core://* URIs that auto-load into downstream agent sessions, enabling persistent prompt-injection. This vulnerability is fixed in 2.4.1.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-27 15:33 UTC
CWE
- CWE-306 - Missing Authentication for Critical Function
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/Dataojitori/nocturne_memory/se… | x_refsource_CONFIRM |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Dataojitori | nocturne_memory |
Affected:
< 2.4.1
|
guessed |
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.