Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2025-27135 is a high-severity SQL Injection (CWE-89) vulnerability in Infiniflow Ragflow. Its CVSS base score is 8.9 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 46th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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-2025-27135 is a SQL injection vulnerability (CWE-89) in RAGFlow, an open-source Retrieval-Augmented Generation (RAG) engine. Versions 0.15.1 and prior are affected, specifically the ExeSQL component, which extracts SQL statements directly from input and executes them on the database without sanitization. The vulnerability was published on 2025-02-25 and carries 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 critical severity.
An unauthenticated remote attacker with network access can exploit this vulnerability through low-complexity attacks requiring no user interaction. Exploitation allows arbitrary SQL query execution, enabling high-impact compromise of confidentiality, integrity, and availability, such as data exfiltration, modification, or deletion from the underlying database.
As of publication, no patched version of RAGFlow is available. Relevant advisories and details are documented in the GitHub security advisory (GHSA-3gqj-66qm-25jq), the affected ExeSQL source code, and external analyses on provided Notion pages.
RAGFlow's role as a RAG engine introduces AI/ML relevance, as deployments in LLM pipelines could expose sensitive data stores to remote compromise. No real-world exploitation has been reported in the available information.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-5395
Vulnerability Data
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine. Versions 0.15.1 and prior are vulnerable to SQL injection. The ExeSQL component extracts the SQL statement from the input and sends it directly to the database query. As of time of publication,…
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no patched version is available.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V6.2.5
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover SQLi flaws before deployment but does not stop their introduction.
Input validation directly stops untrusted data from reaching SQL query construction without neutralization.
Secure engineering principles require parameterized queries and input sanitization that structurally eliminate SQLi.
System monitoring can identify attempted SQLi exploitation via anomalous queries after the weakness exists.
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 target injection flaws during coding and review so largely prevent CWE-89 introduction, yet the single broad outcome leaves residual risk from incomplete neutralization techniques or missed edge cases.
Training raises developer awareness of SQLi risks and can reduce introduction likelihood (partial) but removes none of the actual coding flaw's risk by itself since technical neutralization is still required.
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.
The same secure-coding and static-analysis activities surface missing neutralization of SQL metacharacters before the system is accepted.
Early warnings and shared best-practice information help organizations apply the latest remediation techniques against SQL-injection vulnerabilities.
Threat-intelligence feeds that surface new SQL-injection campaigns enable rapid updates to query-construction defenses and detection signatures before exploitation occurs.
Secure-coding rules and security testing phases mandate the use of parameterized queries or equivalent escaping, preventing the construction of dynamic SQL statements from untrusted input.
Language-specific secure coding rules, peer review and SAST together prevent the construction of SQL statements from untrusted data without proper parameterization or escaping.