CVE-2022-34668
Published: 29 August 2022
Summary
CVE-2022-34668 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Nvidia Nvflare. Its CVSS base score is 9.8 (Critical).
Operationally, ranked in the top 4.0% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
Deeper analysis
NVFLARE versions prior to 2.1.4 contain a deserialization of untrusted data vulnerability stemming from unsafe use of Python Pickle. The affected component is the NVFLARE federated learning framework developed by NVIDIA, which processes serialized data over the network as part of its core communication and job execution paths.
An unauthenticated network attacker can supply malicious pickled payloads to trigger remote code execution, denial of service, or impacts to confidentiality and integrity. The flaw requires no user interaction or privileges, consistent with its CVSS 9.8 rating under CWE-502.
The NVIDIA GitHub Security Advisory GHSA-6qv6-q77g-7qm6 and associated disclosures recommend upgrading to version 2.1.4 or later to eliminate the unsafe pickle usage. Public exploit references such as the PacketStorm entry indicate proof-of-concept material has been published, while the EPSS score reached a peak of 0.2985 after disclosure before receding to its current value of 0.2245.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-0168
Vulnerability details
NVFLARE, versions prior to 2.1.4, contains a vulnerability that deserialization of Untrusted Data due to Pickle usage may allow an unprivileged network attacker to cause Remote Code Execution, Denial Of Service, and Impact to both Confidentiality and Integrity.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Evaluation of untrusted data handling (deserialization testing) reveals unsafe processing, which the required remediation process addresses.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
Validates or rejects untrusted serialized data before deserialization occurs.
Identifies and blocks malicious code introduced through deserialization of untrusted data at system boundaries.
Integrity verification of serialized information can detect tampering before deserialization occurs.
Provenance of associated data allows detection of untrusted sources before deserialization or processing occurs.