CVE-2024-27348 (GCVE-0-2024-27348)
Vulnerability from cvelistv5 – Published: 2024-04-22 14:08 – Updated: 2025-10-21 23:05Title
Apache HugeGraph-Server: Command execution in gremlin
Summary
RCE-Remote Command Execution vulnerability in Apache HugeGraph-Server.This issue affects Apache HugeGraph-Server: from 1.0.0 before 1.3.0 in Java8 & Java11
Users are recommended to upgrade to version 1.3.0 with Java11 & enable the Auth system, which fixes the issue.
Severity
9.8 (Critical)
SSVC
Exploitation: active
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-09-25 03:55 UTC
CWE
- RCE-Remote Command Execution
- CWE-284 - Improper Access Control
Assigner
References
5 references
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| Apache Software Foundation | Apache HugeGraph-Server |
Affected:
1.0.0 , < 1.3.0
(semver)
|
|
| apache | hugegraph-server |
Affected:
1.0.0 , < 1.3.0
(semver)
cpe:2.3:a:apache:hugegraph-server:*:*:*:*:*:*:*:* |
CISA
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: badb40c5-15cc-437e-992c-5362703535a6
Exploited: Yes
Timestamps
First Seen: 2024-09-18
Asserted: 2024-09-18
Scope
Notes: Apache HugeGraph-Server Improper Access Control Vulnerability | Affected: Apache / HugeGraph-Server | Description: Apache HugeGraph-Server contains an improper access control vulnerability that could allow a remote attacker to execute arbitrary code. | Required action: Apply mitigations per vendor instructions or discontinue use of the product if mitigations are unavailable. | Due date: 2024-10-09 | Known ransomware campaign use (KEV): Unknown | Notes (KEV): This vulnerability affects a common open-source component, third-party library, or a protocol used by different products. Please check with specific vendors for information on patching status. For more information, please see: https://lists.apache.org/thread/nx6g6htyhpgtzsocybm242781o8w5kq9 ; https://nvd.nist.gov/vuln/detail/CVE-2024-27348
Evidence
Type: Vendor Report
Signal: Successful Exploitation
Confidence: 80%
Source: cisa-kev
Details
| Cwes | CWE-284 |
|---|---|
| Feed | CISA Known Exploited Vulnerabilities Catalog |
| Product | HugeGraph-Server |
| Due Date | 2024-10-09 |
| Date Added | 2024-09-18 |
| Vendorproject | Apache |
| Vulnerabilityname | Apache HugeGraph-Server Improper Access Control Vulnerability |
| Knownransomwarecampaignuse | Unknown |
References
Created: 2026-10-02 08:09 CEST
| Updated: 2026-10-02 08:09 CEST
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: dbd73931-3ddd-4006-b89e-d96d65ec1eba
Exploited: Yes
Timestamps
First Seen: 2024-09-18
Asserted: 2024-09-18
Scope
Notes: Apache HugeGraph-Server: Command execution in gremlin | Affected: Apache / Apache HugeGraph-Server | CVSS: 9.8 (CRITICAL) | EPSS: 0.9921 | Used in malware: unknown | Not yet in CISA KEV: False
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: previdian
Details
| Feed | Previdian (previdian.com) |
|---|---|
| Title | Apache HugeGraph-Server: Command execution in gremlin |
| Cve Id | CVE-2024-27348 |
| Vendor | Apache |
| Ghsa Id | GHSA-29RC-VQ7F-X335 |
| Product | Apache HugeGraph-Server |
| Added Date | 2024-09-18T00:00:00.000Z |
| Cvss Score | 9.8 |
| Epss Score | 0.9921 |
| Previous Ids | |
| Cvss Severity | CRITICAL |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.99935 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2024-27348 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | False |
References
Created: 2026-10-02 09:11 CEST
| Updated: 2026-10-02 09:11 CEST
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"discovery": "EXTERNAL"
},
"title": "Apache HugeGraph-Server: Command execution in gremlin",
"x_generator": {
"engine": "Vulnogram 0.1.0-dev"
}
}
},
"cveMetadata": {
"assignerOrgId": "f0158376-9dc2-43b6-827c-5f631a4d8d09",
"assignerShortName": "apache",
"cveId": "CVE-2024-27348",
"datePublished": "2024-04-22T14:08:06.294Z",
"dateReserved": "2024-02-24T10:43:56.141Z",
"dateUpdated": "2025-10-21T23:05:21.096Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.1"
}
}
}
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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.
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.
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.
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