CVE-2026-19594 (GCVE-0-2026-19594)
Vulnerability from cvelistv5 – Published: 2026-08-12 05:22 – Updated: 2026-08-12 12:33
VLAI
EPSS
VEX
Title
Path Traversal and HTTP Parameter Pollution in Snowflake Python API (snowflake.core) Allow Confused-Deputy Privilege Escalation
Summary
Insufficient input sanitization in Snowflake Python API (`snowflake.core`) versions prior to 1.13.0 allowed confused-deputy privilege escalation through two related weaknesses: path traversal (CWE-22) via unencoded `..` identifier path segments, and HTTP parameter pollution (CWE-141) via unencoded `&`/`#`/`=` characters in query string values. An attacker with access to a downstream application built on snowflake.core could exploit the path traversal by supplying `..` as an object name, causing `snowflake.core` to issue REST requests against a parent resource or exploit the parameter pollution by injecting `&`/`#`/`=` into a free-form name field to override constraints on swap, clone, or rename operations — all executed under the application's privileged session. Successful exploitation requires the attacker to control an identifier or object-name string in an application built on snowflake.core that passes it to `snowflake.core` under a higher-privileged Snowflake session (e.g., an EXECUTE AS OWNER stored procedure, Streamlit app, or Native App). The fix is available in Snowflake Python API version 1.13.0, which also addresses several additional security findings. Users must manually upgrade.
Severity
8.1 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-08-12 12:33 UTC
CWE
Assigner
References
1 reference
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| Snowflake | Snowflake Python APIs |
Affected:
0 , < 1.13.0
(python)
|
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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.
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.
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