FKIE_CVE-2018-1061
Vulnerability from fkie_nvd - Published: 2018-06-19 12:29 - Updated: 2026-06-17 01:50
Severity
6.5 (Medium) - CVSS:3.0/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:H
7.5 (High) - CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
7.5 (High) - CVSS:3.0/
Summary
python before versions 2.7.15, 3.4.9, 3.5.6rc1, 3.6.5rc1 and 3.7.0 is vulnerable to catastrophic backtracking in the difflib.IS_LINE_JUNK method. An attacker could use this flaw to cause denial of service.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| python | python | * | |
| python | python | * | |
| python | python | * | |
| python | python | * | |
| python | python | 3.7.0 | |
| python | python | 3.7.0 | |
| python | python | 3.7.0 | |
| python | python | 3.7.0 | |
| python | python | 3.7.0 | |
| python | python | 3.7.0 | |
| python | python | 3.7.0 | |
| python | python | 3.7.0 | |
| python | python | 3.7.0 | |
| python | python | 3.7.0 | |
| debian | debian_linux | 8.0 | |
| debian | debian_linux | 9.0 | |
| redhat | ansible_tower | 3.3 | |
| redhat | enterprise_linux_desktop | 7.0 | |
| redhat | enterprise_linux_server | 7.0 | |
| redhat | enterprise_linux_workstation | 7.0 | |
| canonical | ubuntu_linux | 12.04 | |
| canonical | ubuntu_linux | 14.04 | |
| canonical | ubuntu_linux | 16.04 | |
| canonical | ubuntu_linux | 18.04 | |
| fedoraproject | fedora | 28 | |
| fedoraproject | fedora | 29 | |
| fedoraproject | fedora | 30 |
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"lang": "en",
"value": "python before versions 2.7.15, 3.4.9, 3.5.6rc1, 3.6.5rc1 and 3.7.0 is vulnerable to catastrophic backtracking in the difflib.IS_LINE_JUNK method. An attacker could use this flaw to cause denial of service."
},
{
"lang": "es",
"value": "python en versiones anteriores a la 2.7.15, 3.4.9, 3.5.6rc1, 3.6.5rc1 y 3.7.0 es vulnerable a backtracking catastr\u00f3fico en el m\u00e9todo difflib.IS_LINE_JUNK. Un atacante podr\u00eda utilizar este fallo para provocar una denegaci\u00f3n de servicio (DoS)."
}
],
"id": "CVE-2018-1061",
"lastModified": "2026-06-17T01:50:23.597",
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"published": "2018-06-19T12:29:00.220",
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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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