CVE-2024-12433
Published: 20 March 2025
Summary
CVE-2024-12433 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Infiniflow Ragflow. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Default Accounts (T1078.001); ranked in the top 12.8% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-6997
Vulnerability details
A vulnerability in infiniflow/ragflow versions v0.12.0 allows for remote code execution. The RPC server in RagFlow uses a hard-coded AuthKey 'authkey=b'infiniflow-token4kevinhu'' which can be easily fetched by attackers to join the group communication without restrictions. Additionally, the server processes incoming…
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data using pickle deserialization via `pickle.loads()` on `connection.recv()`, making it vulnerable to remote code execution. This issue is fixed in version 0.14.0.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Hard-coded AuthKey enables use of default/known accounts for unauthorized access (T1078.001). Unsafe pickle deserialization allows remote code execution via exploitation of the public-facing RPC server (T1190).
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.