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-2026-22607 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 38th 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-2026-22607 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 cProfile module as unsafe, resulting in malicious pickles that invoke cProfile.run() being labeled as SUSPICIOUS rather than OVERTLY_MALICIOUS. This misclassification affects any workflow or product that depends on Fickling's analysis as a security gate prior to pickle deserialization, potentially tricking users into processing dangerous data. The issue is rated at CVSS 7.8 (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 with local access can craft a malicious pickle file that leverages cProfile.run() to execute arbitrary code upon deserialization. Exploitation requires a user to interact with Fickling's output—such as reviewing its SUSPICIOUS classification and deciding to proceed with deserialization—leading to full compromise of confidentiality, integrity, and availability on the victim's system through attacker-controlled code execution. No privileges are needed (PR:N), but the attack is local (AV:L) and low complexity (AC:L).
The vulnerability has been addressed in Fickling version 0.1.7, where cProfile is now properly treated as unsafe. Security advisories and the patch commit are available on the project's GitHub repository, including the release notes for v0.1.7 and the GHSA-p523-jq9w-64x9 advisory, recommending immediate upgrade for users relying on Fickling for pickle safety checks.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-1687
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 cProfile module as unsafe. Because of this, a malicious pickle that uses cProfile.run() is classified as SUSPICIOUS instead of OVERTLY_MALICIOUS.…
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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.