CVE-2017-1000158 (GCVE-0-2017-1000158)
Vulnerability from cvelistv5 – Published: 2017-11-17 00:00 – Updated: 2024-08-05 21:53
VLAI
EPSS
VEX
Summary
CPython (aka Python) up to 2.7.13 is vulnerable to an integer overflow in the PyString_DecodeEscape function in stringobject.c, resulting in heap-based buffer overflow (and possible arbitrary code execution)
Severity
9.8 (Critical)
CWE
- n/a
Assigner
References
9 references
| URL | Tags |
|---|---|
| https://lists.debian.org/debian-lts-announce/2017… | mailing-list |
| https://lists.debian.org/debian-lts-announce/2018… | mailing-list |
| https://bugs.python.org/issue30657 | |
| http://www.securitytracker.com/id/1039890 | vdb-entry |
| https://lists.debian.org/debian-lts-announce/2017… | mailing-list |
| https://www.debian.org/security/2018/dsa-4307 | vendor-advisory |
| https://lists.debian.org/debian-lts-announce/2018… | mailing-list |
| https://security.gentoo.org/glsa/201805-02 | vendor-advisory |
| https://security.netapp.com/advisory/ntap-2023021… |
Date Public
2017-11-16 00:00
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}
},
"redhat_vex": {
"aggregate_severity": "Moderate",
"current_release_date": "2026-05-13T01:38:02+00:00",
"cve": "CVE-2017-1000158",
"id": "CVE-2017-1000158",
"initial_release_date": "2017-06-13T00:00:00+00:00",
"product_status:known_affected": "40",
"product_status:known_not_affected": "8",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "python: Integer overflow in PyString_DecodeEscape results in heap-base buffer overflow",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2017/cve-2017-1000158.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "important",
"current_release_date": "2026-09-05T23:51:19Z",
"cve": "CVE-2017-1000158",
"id": "CVE-2017-1000158",
"initial_release_date": "2023-02-15T04:35:20Z",
"product_status:known_affected": "484",
"product_status:known_not_affected": "18",
"product_status:recommended": "783",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2017-1000158",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2017-1000158.json",
"version": "40"
}
}
}
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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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