FKIE_CVE-2020-10878
Vulnerability from fkie_nvd - Published: 2020-06-05 14:15 - Updated: 2026-06-17 02:48
Severity
Summary
Perl before 5.30.3 has an integer overflow related to mishandling of a "PL_regkind[OP(n)] == NOTHING" situation. A crafted regular expression could lead to malformed bytecode with a possibility of instruction injection.
References
Impacted products
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"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Perl before 5.30.3 has an integer overflow related to mishandling of a \"PL_regkind[OP(n)] == NOTHING\" situation. A crafted regular expression could lead to malformed bytecode with a possibility of instruction injection."
},
{
"lang": "es",
"value": "Perl versiones anteriores a 5.30.3, presenta un desbordamiento de enteros relacionado con un manejo inapropiado de una situaci\u00f3n \"PL_regkind[OP(n)] == NOTHING\". Una expresi\u00f3n regular dise\u00f1ada podr\u00eda conllevar a un bytecode malformado con la posibilidad de inyecci\u00f3n de instrucciones"
}
],
"id": "CVE-2020-10878",
"lastModified": "2026-06-17T02:48:36.157",
"metrics": {
"cvssMetricV2": [
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"acInsufInfo": false,
"baseSeverity": "HIGH",
"cvssData": {
"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 7.5,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
"version": "2.0"
},
"exploitabilityScore": 10.0,
"impactScore": 6.4,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
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"attackVector": "NETWORK",
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"baseScore": 8.6,
"baseSeverity": "HIGH",
"confidentialityImpact": "LOW",
"integrityImpact": "LOW",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:H",
"version": "3.1"
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"exploitabilityScore": 3.9,
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"source": "nvd@nist.gov",
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"published": "2020-06-05T14:15:10.527",
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"sourceIdentifier": "cve@mitre.org",
"vulnStatus": "Modified",
"weaknesses": [
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"value": "CWE-190"
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}
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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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