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-2026-22606 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 35th 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.
CVE-2026-22606 is a vulnerability in Fickling, an open-source Python pickling decompiler and static analyzer. Versions up to and including 0.1.6 fail to classify the use of Python's runpy module as unsafe, specifically runpy.run_path() or runpy.run_module(). As a result, malicious pickles invoking these functions are labeled SUSPICIOUS rather than OVERTLY_MALICIOUS. This misclassification affects any workflows or products that depend on Fickling's analysis as a security gate prior to pickle deserialization, potentially tricking users into processing unsafe data. The issue is rated 7.8 on the CVSS v3.1 scale (AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H) and maps to CWE-184 (Incomplete List of Disallowed Inputs) and CWE-502 (Deserialization of Untrusted Data).
An attacker can exploit this by crafting a pickle that leverages runpy to execute arbitrary code, then providing it to a target using vulnerable Fickling versions for safety checks. Exploitation requires local access to deliver the pickle and user interaction to trigger deserialization despite the SUSPICIOUS rating, with no privileges needed. Successful exploitation allows the attacker to achieve high-impact confidentiality, integrity, and availability violations through code execution on the victim's system.
The vulnerability has been patched in Fickling version 0.1.7, as detailed in the project's GitHub security advisory (GHSA-wfq2-52f7-7qvj), release notes, and the fixing commit (9a2b3f89bd0598b528d62c10a64c1986fcb09f66). Security practitioners using Fickling should upgrade to 0.1.7 or later and avoid relying solely on its classifications for untrusted pickle deserialization decisions.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-1688
Vulnerability Data
Fickling is a Python pickling decompiler and static analyzer. Fickling versions up to and including 0.1.6 do not treat Python’s runpy module as unsafe. Because of this, a malicious pickle that uses runpy.run_path() or runpy.run_module() is classified as SUSPICIOUS instead…
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of OVERTLY_MALICIOUS. If a user relies on Fickling’s output to decide whether a pickle is safe to deserialize, this misclassification can lead them to execute attacker-controlled code on their system. This affects any workflow or product that uses Fickling as a security gate for pickle deserialization. This issue has been patched in version 0.1.7.
- 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.