CVE-2025-67747
Dec. 16, 2025, 2:10 p.m.
7.1
High
Description
Fickling is a Python pickling decompiler and static analyzer. Versions prior to 0.1.6 are missing `marshal` and `types` from the block list of unsafe module imports. Fickling started blocking both modules to address this issue. This allows an attacker to craft a malicious pickle file that can bypass fickling since it misses detections for `types.FunctionType` and `marshal.loads`. A user who deserializes such a file, believing it to be safe, would inadvertently execute arbitrary code on their system. This impacts any user or system that uses Fickling to vet pickle files for security issues. The issue was fixed in version 0.1.6.
Product(s) Impacted
| Product | Versions |
|---|
Weaknesses
Common security weaknesses mapped to this vulnerability.
CWE-184
Incomplete List of Disallowed Inputs
The product implements a protection mechanism that relies on a list of inputs (or properties of inputs) that are not allowed by policy or otherwise require other action to neutralize before additional processing takes place, but the list is incomplete, leading to resultant weaknesses.
Tags
CVSS Score
CVSS Data - 4.0
- Attack Vector: LOCAL
- Attack Complexity: LOW
- Attack Requirements: NONE
- Privileges Required: NONE
- User Interaction: PASSIVE
- Scope:
- Confidentiality Impact: HIGH
- Integrity Impact: HIGH
- Availability Impact: HIGH
- Exploit Maturity: PROOF_OF_CONCEPT
CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:P/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
Timeline
Published: Dec. 16, 2025, 1:15 a.m.
Last Modified: Dec. 16, 2025, 2:10 p.m.
Last Modified: Dec. 16, 2025, 2:10 p.m.
Status : Undergoing Analysis
CVE is currently being analyzed by NVD staff, this process results in association of reference link tags, CVSS scores, CWE association, and CPE applicability statements.
More infoSource
security-advisories@github.com
*Disclaimer: Some vulnerabilities do not have an associated CPE. To enhance the data, we use AI to infer CPEs based on CVE details. This is an automated process and might not always be accurate.