FKIE_CVE-2009-1391
Vulnerability from fkie_nvd - Published: 2009-06-16 23:30 - Updated: 2026-06-16 23:07
Severity
Summary
Off-by-one error in the inflate function in Zlib.xs in Compress::Raw::Zlib Perl module before 2.017, as used in AMaViS, SpamAssassin, and possibly other products, allows context-dependent attackers to cause a denial of service (hang or crash) via a crafted zlib compressed stream that triggers a heap-based buffer overflow, as exploited in the wild by Trojan.Downloader-71014 in June 2009.
References
Impacted products
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"source": "secalert@redhat.com"
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"cveTags": [],
"descriptions": [
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"lang": "en",
"value": "Off-by-one error in the inflate function in Zlib.xs in Compress::Raw::Zlib Perl module before 2.017, as used in AMaViS, SpamAssassin, and possibly other products, allows context-dependent attackers to cause a denial of service (hang or crash) via a crafted zlib compressed stream that triggers a heap-based buffer overflow, as exploited in the wild by Trojan.Downloader-71014 in June 2009."
},
{
"lang": "es",
"value": "Error de superaci\u00f3n de l\u00edmite (Off-by-one) en la funci\u00f3n \"inflate\" en Zlib.xs en el modulo de Perl Compress::Raw::Zlib anteriores a v2.017, cuando es utilizado en AMaVis, SpamAssassin, y posiblemente otros productos, permite a atacantes dependientes del contexto, producir una denegaci\u00f3n de servicio (cuelgue o ca\u00edda) a trav\u00e9s de un stream comprimido zlib manipulado que inicia un desbordamiento de b\u00fafer basado en memoria libre, como se explot activamente por Trojan.Downloader-71014 en Junio de 2009."
}
],
"id": "CVE-2009-1391",
"lastModified": "2026-06-16T23:07:11.350",
"metrics": {
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"acInsufInfo": false,
"baseSeverity": "MEDIUM",
"cvssData": {
"accessComplexity": "MEDIUM",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 6.8,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:M/Au:N/C:P/I:P/A:P",
"version": "2.0"
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"source": "nvd@nist.gov",
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"published": "2009-06-16T23:30:00.203",
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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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