Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2025-61622 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Apache Fory. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 1% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
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-2025-61622 is a deserialization of untrusted data vulnerability (CWE-502) in the Python libraries pyfory versions 0.12.0 through 0.12.2 and legacy pyfury versions 0.1.0 through 0.10.3. It enables arbitrary code execution when an application deserializes pyfory serialized data from untrusted sources. Specifically, an attacker can craft a malicious data stream that forces the use of a pickle-fallback serializer during deserialization, triggering the execution of the insecure `pickle.loads` function.
The vulnerability has a CVSS v3.1 base score of 9.8 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H), indicating it is exploitable over the network with low complexity, no privileges or user interaction required. Any remote attacker capable of supplying crafted serialized data to a vulnerable application can achieve arbitrary code execution on the target system, potentially leading to full compromise.
Advisories recommend upgrading to pyfory version 0.12.3 or later, which removes the pickle fallback serializer and resolves the issue. Additional details are available in the Apache mailing list announcement at https://lists.apache.org/thread/vfn9hp9qt06db5yo1gmj3l114o3o2csd and the oss-security mailing list at http://www.openwall.com/lists/oss-security/2025/09/29/3.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-32708
Vulnerability Data
Deserialization of untrusted data in python in pyfory versions 0.12.0 through 0.12.2, or the legacy pyfury versions from 0.1.0 through 0.10.3: allows arbitrary code execution. An application is vulnerable if it reads pyfory serialized data from untrusted sources. An attacker…
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can craft a data stream that selects pickle-fallback serializer during deserialization, leading to the execution of `pickle.loads`, which is vulnerable to remote code execution. Users are recommended to upgrade to pyfory version 0.12.3 or later, which has removed pickle fallback serializer and thus fixes this issue.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
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.
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 includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
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.