Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/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-2026-22609 is a high-severity Incomplete List of Disallowed Inputs (CWE-184) vulnerability in Trailofbits Fickling. Its CVSS base score is 8.9 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 43th 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.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
Fickling, a Python pickling decompiler and static analyzer, contains a vulnerability prior to version 0.1.7 where the unsafe_imports() method in its static analyzer fails to flag several high-risk Python modules capable of arbitrary code execution. As a result, malicious pickles that import these modules evade detection, bypassing Fickling's primary static safety checks. This flaw is classified under CWE-184 and CWE-502, with a CVSS v3.1 base score of 7.8.
An attacker with local access can exploit this vulnerability by crafting a malicious pickle file that imports undetected high-risk modules and tricking a user into analyzing it with an affected version of Fickling. The low attack complexity and requirement for user interaction (such as running the analyzer on the file) allow no privileges to achieve high-impact confidentiality, integrity, and availability violations, potentially leading to arbitrary code execution if the falsely safe pickle is subsequently unpickled.
The issue has been addressed in Fickling version 0.1.7, as detailed in the project's GitHub release and related commit history, which include fixes to properly flag the risky modules in unsafe_imports(). Security practitioners should update to this version or later to mitigate the vulnerability.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-1685
Vulnerability Data
Fickling is a Python pickling decompiler and static analyzer. Prior to version 0.1.7, the unsafe_imports() method in Fickling's static analyzer fails to flag several high-risk Python modules that can be used for arbitrary code execution. Malicious pickles importing these modules…
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will not be detected as unsafe, allowing attackers to bypass Fickling's primary static safety checks. This issue has been patched in version 0.1.7.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
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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.