SUSE-SU-2025:03257-1
Vulnerability from csaf_suse - Published: 2025-09-17 19:01 - Updated: 2025-09-17 19:01Summary
Security update for raptor
Severity
Moderate
Notes
Title of the patch: Security update for raptor
Description of the patch: This update for raptor fixes the following issues:
- CVE-2024-57823: Fixed integer underflow when normalizing a URI with the turtle parser (bsc#1235673).
- CVE-2024-57822: Fixed heap buffer overread when parsing triples with the nquads parser (bsc#1235674).
Patchnames: SUSE-2025-3257,SUSE-SLE-SERVER-12-SP5-LTSS-EXTENDED-SECURITY-2025-3257
Terms of use: CSAF 2.0 data is provided by SUSE under the Creative Commons License 4.0 with Attribution (CC-BY-4.0).
6.6 (Medium)
Affected products
Recommended
2 products
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: SUSE Linux Enterprise Server LTSS Extended Security 12 SP5:libraptor-devel-2.0.15-5.9.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: SUSE Linux Enterprise Server LTSS Extended Security 12 SP5:libraptor2-0-2.0.15-5.9.1.x86_64 | — |
Vendor Fix
|
Threats
Impact
moderate
6.6 (Medium)
Affected products
Recommended
2 products, the same list as for
CVE-2024-57822
Threats
Impact
moderate
References
12 references
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"vulnerabilities": [
{
"cve": "CVE-2024-57822",
"ids": [
{
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"text": "https://www.suse.com/security/cve/CVE-2024-57822"
}
],
"notes": [
{
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"text": "In Raptor RDF Syntax Library through 2.0.16, there is a heap-based buffer over-read when parsing triples with the nquads parser in raptor_ntriples_parse_term_internal().",
"title": "CVE description"
}
],
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"details": "To install this SUSE Security Update use the SUSE recommended installation methods like YaST online_update or \"zypper patch\".\n",
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]
}
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"baseSeverity": "MEDIUM",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H",
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},
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"text": "In Raptor RDF Syntax Library through 2.0.16, there is an integer underflow when normalizing a URI with the turtle parser in raptor_uri_normalize_path().",
"title": "CVE description"
}
],
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"date": "2025-09-17T19:01:57Z",
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"title": "CVE-2024-57823"
}
]
}
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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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