CVE-2018-17247 (GCVE-0-2018-17247)
Vulnerability from cvelistv5 – Published: 2018-12-20 22:00 – Updated: 2024-08-05 10:47
VLAI
EPSS
VEX
Summary
Elasticsearch Security versions 6.5.0 and 6.5.1 contain an XXE flaw in Machine Learning's find_file_structure API. If a policy allowing external network access has been added to Elasticsearch's Java Security Manager then an attacker could send a specially crafted request capable of leaking content of local files on the Elasticsearch node. This could allow a user to access information that they should not have access to.
Severity
No CVSS data available.
CWE
- CWE-611 - Improper Restriction of XML External Entity Reference ('XXE')
Assigner
References
3 references
| URL | Tags |
|---|---|
| http://www.securityfocus.com/bid/106294 | vdb-entryx_refsource_BID |
| https://discuss.elastic.co/t/elastic-stack-6-5-2-… | x_refsource_MISC |
| https://www.elastic.co/community/security | x_refsource_CONFIRM |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| Elastic | Elasticsearch |
Affected:
6.5.0 and 6.5.1
|
Date Public
2018-12-20 00:00
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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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