Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2026-33350 is a high-severity SQL Injection (CWE-89) vulnerability in Mcgill Loris. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 16th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
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-2026-33350 is a SQL injection vulnerability (CWE-89) in LORIS, a self-hosted web application used for data and project management in neuroimaging research. The flaw affects versions prior to 27.0.3 and 28.0.1, specifically in code sections handling the MRI feedback popup window of the imaging browser. It carries a CVSS v3.1 base score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N) and was published on 2026-04-08T19:25:21.163.
Unauthenticated remote attackers can exploit this vulnerability over the network with low attack complexity and no user interaction required. Exploitation enables SQL injection to access sensitive data on the server and potentially alter it, resulting in high confidentiality impact but no integrity or availability disruption per the CVSS assessment.
The vulnerability is addressed in LORIS releases 27.0.3 and 28.0.1. Additional details on the issue and mitigation are available in the GitHub security advisory at https://github.com/aces/Loris/security/advisories/GHSA-9r29-6jgc-3ggh.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-20552
Vulnerability Data
LORIS (Longitudinal Online Research and Imaging System) is a self-hosted web application that provides data- and project-management for neuroimaging research. Prior to 27.0.3 and 28.0.1, a SQL injection has been identified in some code sections for the MRI feedback popup…
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window of the imaging browser. Attackers can use SQL ingestion to access/alter data on the server. This vulnerability is fixed in 27.0.3 and 28.0.1.
- 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.