CVE-2024-10906
CSRF in Dbgpt Db-Gpt 0.6.0
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:H/A:HSummary
CVE-2024-10906 is a high-severity CSRF (CWE-352) vulnerability in Dbgpt Db-Gpt. Its CVSS base score is 8.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 14th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and SC-23 (Session Authenticity) — 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-2024-10906 is a Cross-Site Request Forgery (CSRF) vulnerability affecting version 0.6.0 of eosphoros-ai/db-gpt. The issue stems from the uvicorn application created by dbgpt_server, which employs an overly permissive CORSMiddleware configuration. This sets the Access-Control-Allow-Origin header to "*" for all requests, exposing all server endpoints to CSRF attacks. The vulnerability is classified under CWE-352 with a CVSS v3.1 base score of 8.1 (AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:H/A:H).
Attackers can exploit this vulnerability without privileges over the network with low complexity, but it requires user interaction, such as tricking a victim into visiting a malicious site. Even if the db-gpt instance is not publicly exposed, an attacker can force the victim's browser to interact with any endpoints on the server, potentially leading to high integrity and availability impacts, such as unauthorized actions or disruptions.
The primary advisory is available at https://huntr.com/bounties/8864aca5-a342-4dab-b866-b2882ba6f160, which details the vulnerability discovered through a bug bounty program. Practitioners should consult this reference for specific patch information or mitigation guidance, such as restricting CORS origins or upgrading to a fixed version if available. The CVE was published on 2025-03-20.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-7072
Vulnerability Data
In version 0.6.0 of eosphoros-ai/db-gpt, the `uvicorn` app created by `dbgpt_server` uses an overly permissive instance of `CORSMiddleware` which sets the `Access-Control-Allow-Origin` to `*` for all requests. This configuration makes all endpoints exposed by the server vulnerable to Cross-Site Request…
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Forgery (CSRF). An attacker can exploit this vulnerability to interact with any endpoints of the instance, even if the instance is not publicly exposed to the network.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai, gpt
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V3.3.2V3.5.1V10.2.1
Mitigating Controls (NIST 800-53 r5) AI
Access enforcement requires verifying that state-changing requests originate from the authenticated user rather than a forged cross-site source.
Protecting session authenticity prevents attackers from replaying or forging authenticated requests via the victim's browser.
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 anti-CSRF controls such as tokens or SameSite attributes.
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.
By denying access to phishing or malicious sites, the control lowers the likelihood that a user will be tricked into submitting a forged request that performs an unintended action on another site.
Contextual intelligence about emerging CSRF toolkits can be translated into updated anti-CSRF token or same-site policy configurations across applications.