Raw vector
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:XSummary
CVE-2025-67747 is a high-severity Incomplete List of Disallowed Inputs (CWE-184) vulnerability in Trailofbits Fickling. Its CVSS base score is 7.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 17th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-203479
Vulnerability Data
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…
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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.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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- 2 hardening rules · 1 OS baseline
V3.5.2V4.4.2V16.2.5
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
SI-10 requires validity checks on inputs, which structurally replaces incomplete deny-lists with complete allow-list or sanitization logic.
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.
Secure SDLC practices directly require complete, positive input validation instead of incomplete denylists.
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 can discover missing input checks, but does not prevent the weakness during development.
Application security requirements can mandate complete input validation rules, but the control itself does not prescribe how to build those rules.
Secure architecture principles include robust input validation design, yet the control is broader than this single weakness.
Secure coding standards directly require exhaustive allow-lists or complete deny-lists for inputs, addressing the root cause of incomplete disallowed-input lists.
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.