CVE-2024-0397 (GCVE-0-2024-0397)
Vulnerability from cvelistv5 – Published: 2024-06-17 15:09 – Updated: 2025-11-03 21:50
VLAI
EPSS
VEX
Title
Memory race condition in ssl.SSLContext certificate store methods
Summary
A defect was discovered in the Python “ssl” module where there is a memory
race condition with the ssl.SSLContext methods “cert_store_stats()” and
“get_ca_certs()”. The race condition can be triggered if the methods are
called at the same time as certificates are loaded into the SSLContext,
such as during the TLS handshake with a certificate directory configured.
This issue is fixed in CPython 3.10.14, 3.11.9, 3.12.3, and 3.13.0a5.
Severity
7.4 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-06-20 15:52 UTC
CWE
- CWE-362 - Concurrent Execution using Shared Resource with Improper Synchronization ('Race Condition')
Assigner
References
12 references
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Python Software Foundation | CPython |
Affected:
0 , < 3.8.20
(python)
Affected: 3.9.0 , < 3.9.20 (python) Affected: 3.10.0 , < 3.10.14 (python) Affected: 3.11.0 , < 3.11.9 (python) Affected: 3.12.0 , < 3.12.3 (python) Affected: 3.13.0a1 , < 3.13.0a5 (python) |
guessed | |
| python_software_foundation | cpython |
Affected:
0 , < 3.8.20
(python)
Affected: 3.9.0 , < 3.9.20 (python) Affected: 3.10.0 , < 3.10.14 (python) Affected: 3.11.0 , < 3.11.9 (python) Affected: 3.12.0 , < 3.12.3 (python) Affected: 3.13.0a1 , < 3.13.0a5 (python) cpe:2.3:a:python_software_foundation:cpython:*:*:*:*:*:*:*:* |
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},
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},
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],
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},
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"url": "https://github.com/python/cpython/commit/bce693111bff906ccf9281c22371331aaff766ab"
},
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},
{
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},
{
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}
],
"title": "CVE Program Container"
},
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],
"metrics": [
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"baseScore": 7.4,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:N/A:H",
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},
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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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