RHSA-2018:2916
Vulnerability from csaf_opensuse - Published: 2018-10-11 00:00 - Updated: 2026-09-20 11:01Summary
security update for spamassassin
Severity
Important
Notes
Title of the patch: security update for spamassassin
Description of the patch: This security update provides a functional equivalent of RHSA-2018:2916.
The original Red Hat(R) advisory is available from the Red Hat web site at
https://access.redhat.com/errata/RHSA-2018:2916.
Patchnames: RHSA-2018:2916
Terms of use: CSAF 2.0 data is provided by SUSE under the Creative Commons License 4.0 with Attribution (CC-BY-4.0).
7.5 (High)
Affected products
Recommended
1 product
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: SUSE Liberty Linux 7:spamassassin-0:3.4.0-4.el7_5.x86_64 | — |
Vendor Fix
|
Threats
Impact
important
7.8 (High)
Affected products
Recommended
1 product, the same list as for
CVE-2017-15705
Threats
Impact
important
References
8 references
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"text": "A denial of service vulnerability was identified that exists in Apache SpamAssassin before 3.4.2. The vulnerability arises with certain unclosed tags in emails that cause markup to be handled incorrectly leading to scan timeouts. In Apache SpamAssassin, using HTML::Parser, we setup an object and hook into the begin and end tag event handlers In both cases, the \"open\" event is immediately followed by a \"close\" event - even if the tag *does not* close in the HTML being parsed. Because of this, we are missing the \"text\" event to deal with the object normally. This can cause carefully crafted emails that might take more scan time than expected leading to a Denial of Service. The issue is possibly a bug or design decision in HTML::Parser that specifically impacts the way Apache SpamAssassin uses the module with poorly formed html. The exploit has been seen in the wild but not believed to have been purposefully part of a Denial of Service attempt. We are concerned that there may be attempts to abuse the vulnerability in the future.",
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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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