CVE-2024-58364 (GCVE-0-2024-58364)
Vulnerability from cvelistv5 – Published: 2026-07-18 13:10 – Updated: 2026-07-28 01:47
VLAI
EPSS
VEX
Title
SurrealDB before 1.2.1 Denial of Service via Parsing Error
Summary
SurrealDB versions before 1.2.1 contain an uncaught exception handling vulnerability in span rendering when parsing queries with errors on line terminator characters. Authorized clients can submit malformed queries that trigger a panic in the span rendering code, crashing the server and causing denial of service.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator (v2.0.3)
CWE
- CWE-248 - Uncaught Exception
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/surrealdb/surrealdb/security/a… | vendor-advisory |
| https://www.vulncheck.com/advisories/surrealdb-be… | third-party-advisory |
Impacted products
Date Public
2024-02-19 00:00
Credits
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
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