Raw vector
CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2025-24794 is a medium-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Snowflake Snowflake Connector. Its CVSS base score is 6.7 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 16th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-24794 is a deserialization vulnerability (CWE-502) in the Snowflake Connector for Python, an interface for developing Python applications that connect to Snowflake and perform standard operations. The issue stems from the OCSP response cache using pickle as the serialization format, which can lead to local privilege escalation. It affects versions 2.7.12 through 3.13.0 of the connector, with a CVSS v3.1 base score of 6.7 (AV:L/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H).
A local attacker with high privileges (PR:H) can exploit this vulnerability with low complexity and no user interaction required. Successful exploitation allows the attacker to achieve high impacts on confidentiality, integrity, and availability, enabling local privilege escalation on the affected system.
Snowflake discovered and remediated the vulnerability, releasing version 3.13.1 as the fix. Detailed information is available in the GitHub security advisory (GHSA-m4f6-vcj4-w5mx) and the specific commit (3769b43822357c3874c40f5e74068458c2dc79af) that addresses the pickle serialization issue in the OCSP cache. Security practitioners should upgrade to version 3.13.1 or later to mitigate the risk.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-0179
Vulnerability Data
The Snowflake Connector for Python provides an interface for developing Python applications that can connect to Snowflake and perform all standard operations. Snowflake discovered and remediated a vulnerability in the Snowflake Connector for Python. The OCSP response cache uses pickle…
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as the serialization format, potentially leading to local privilege escalation. This vulnerability affects versions 2.7.12 through 3.13.0. Snowflake fixed the issue in version 3.13.1.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.