CVE-2021-41732 (GCVE-0-2021-41732)
Vulnerability from cvelistv5 – Published: 2021-09-29 18:34 – Updated: 2024-08-04 03:15 Disputed
VLAI
EPSS
VEX
Summary
An issue was discovered in zeek version 4.1.0. There is a HTTP request splitting vulnerability that will invalidate any ZEEK HTTP based security analysis. NOTE: the vendor's position is that the observed behavior is intended
Severity
8.2 (High)
SSVC
Exploitation: poc
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-05-01 14:22 UTC
CWE
- n/a
- CWE-444 - Inconsistent Interpretation of HTTP Requests ('HTTP Request/Response Smuggling')
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/zeek/zeek/issues/1798 | x_refsource_MISC |
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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.
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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.