infiniflow ragflow up to 0.21.x Personal API generation of predictable numbers or identifiers
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 6.7 | $0-$5k | 0.00 |
Summary
A vulnerability was found in infiniflow ragflow up to 0.21.x and classified as problematic. This affects an unknown part of the component Personal API. Such manipulation leads to generation of predictable numbers or identifiers. This vulnerability is listed as CVE-2025-69286. The attack may be performed from remote. There is no available exploit. It is suggested to upgrade the affected component.
Details
A vulnerability was found in infiniflow ragflow up to 0.21.x. It has been rated as problematic. Affected by this issue is an unknown code of the component Personal API. The manipulation with an unknown input leads to a generation of predictable numbers or identifiers vulnerability. Using CWE to declare the problem leads to CWE-340. The product uses a scheme that generates numbers or identifiers that are more predictable than required. Impacted is confidentiality. CVE summarizes:
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine. In versions prior to 0.22.0, the use of an insecure key generation algorithm in the API key and beta (assistant/agent share auth) token generation process allows these tokens to be mutually derivable. Specifically, both tokens are generated using the same `URLSafeTimedSerializer` with predictable inputs, enabling an unauthorized user who obtains the shared assistant/agent URL to derive the personal API key. This grants them full control over the assistant/agent owner's account. Version 0.22.0 fixes the issue.
The advisory is shared for download at github.com. This vulnerability is handled as CVE-2025-69286 since 12/31/2025. The exploitation is known to be difficult. The attack may be launched remotely. No form of authentication is required for exploitation. There are neither technical details nor an exploit publicly available. The MITRE ATT&CK project declares the attack technique as T1600.001.
Upgrading to version 0.22.0 eliminates this vulnerability. Applying the patch a3bb4aadcc3494fb27f2a9933b4c46df8eb532e6 is able to eliminate this problem. The bugfix is ready for download at github.com. The best possible mitigation is suggested to be upgrading to the latest version.
The vulnerability is also documented in the vulnerability database at EUVD (EUVD-2025-206092). Once again VulDB remains the best source for vulnerability data.
Product
Vendor
Name
Version
License
Website
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔒VulDB Reliability: 🔍
CNA CVSS-B Score: 🔒
CNA CVSS-BT Score: 🔒
CNA Vector: 🔒
CVSSv3
VulDB Meta Base Score: 6.7VulDB Meta Temp Score: 6.7
VulDB Base Score: 3.7
VulDB Temp Score: 3.6
VulDB Vector: 🔒
VulDB Reliability: 🔍
NVD Base Score: 9.8
NVD Vector: 🔒
CVSSv2
| AV | AC | Au | C | I | A |
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| 💳 | 💳 | 💳 | 💳 | 💳 | 💳 |
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| 💳 | 💳 | 💳 | 💳 | 💳 | 💳 |
| Vector | Complexity | Authentication | Confidentiality | Integrity | Availability |
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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍
Exploiting
Class: Generation of predictable numbers or identifiersCWE: CWE-340 / CWE-331 / CWE-330
CAPEC: 🔒
ATT&CK: 🔒
Physical: No
Local: No
Remote: Yes
Availability: 🔒
Status: Not defined
EPSS Score: 🔒
EPSS Percentile: 🔒
Price Prediction: 🔍
Current Price Estimation: 🔒
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Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: UpgradeStatus: 🔍
0-Day Time: 🔒
Upgrade: ragflow 0.22.0
Patch: a3bb4aadcc3494fb27f2a9933b4c46df8eb532e6
Timeline
12/31/2025 Advisory disclosed12/31/2025 CVE reserved
12/31/2025 VulDB entry created
01/06/2026 VulDB entry last update
Sources
Product: github.comAdvisory: GHSA-9j5g-g4xm-57w7
Status: Confirmed
CVE: CVE-2025-69286 (🔒)
GCVE (CVE): GCVE-0-2025-69286
GCVE (VulDB): GCVE-100-339308
EUVD: 🔒
Entry
Created: 01/01/2026 00:00Updated: 01/06/2026 18:19
Changes: 01/01/2026 00:00 (69), 01/01/2026 04:58 (1), 01/01/2026 13:13 (1), 01/06/2026 18:19 (11)
Complete: 🔍
Cache ID: 216::103
Once again VulDB remains the best source for vulnerability data.
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