CVE-2026-102938 (GCVE-0-2026-102938)
Vulnerability from cvelistv5 – Published: 2026-09-29 20:58 – Updated: 2026-09-30 19:53
VLAI
EPSS
VEX
Title
virtualenv writes prompt values into pyvenv.cfg without sanitizing line boundaries, allowing configuration injection
Summary
virtualenv is a tool for creating isolated virtual python environments. Prior to 21.7.11, PyEnvCfg.write() writes prompt values verbatim to the line-oriented pyvenv.cfg format while PyEnvCfg._read_values() parses the file with str.splitlines() and accepts the last value for duplicate keys. An attacker who influences --prompt, VIRTUALENV_PROMPT, or configuration input can insert a recognized line boundary and additional keys, including home, causing consumers to use an attacker-selected base interpreter or corrupted environment metadata. The security impact requires prompt input from outside the operator's trust boundary; directly supplied prompt content primarily corrupts the operator's own environment. This issue is fixed in version 21.7.11.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-30 19:53 UTC
CWE
- CWE-93 - Improper Neutralization of CRLF Sequences ('CRLF Injection')
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/pypa/virtualenv/security/advis… | x_refsource_CONFIRM |
| https://github.com/pypa/virtualenv/pull/3247 | x_refsource_MISC |
| https://github.com/pypa/virtualenv/commit/a30f995… | x_refsource_MISC |
| https://github.com/pypa/virtualenv/releases/tag/21.7.11 | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| pypa | virtualenv |
Affected:
< 21.7.11
|
guessed |
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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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