Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/E:X/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-2026-29080 is a critical-severity SQL Injection (CWE-89) vulnerability in Cern Rucio. Its CVSS base score is 9.4 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 20th 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.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-27869
Vulnerability Data
A SQL injection vulnerability in `FilterEngine.create_sqla_query()` allows any authenticated Rucio user to execute arbitrary SQL against the backend database through the DID search endpoint (`GET /dids/<scope>/dids/search`). On Oracle deployments attacker-controlled filter keys and values are interpolated directly into `sqlalchemy.text()` via…
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Python `.format()`, completely bypassing parameterization. This enables full database compromise including extraction of authentication tokens, password hashes, and all managed data identifiers. This affects versions 1.27.0 and later before 35.8.5, 38.5.5, 39.4.2, and 40.1.1. The vulnerability exists in `lib/rucio/core/did_meta_plugins/filter_engine.py` within the `create_sqla_query()` method. When the database dialect is Oracle, filter expressions for JSON metadata columns are constructed using `text()` with Python string formatting. Both `key` and `value` are attacker-controlled strings derived from HTTP query parameters. The `text()` function creates a raw SQL fragment — it does **not** escape or parameterize its contents. Any authenticated Rucio user can exploit this through the DID search API to execute arbitrary SQL against the backend database. This can expose all managed data identifiers and sensitive tables such as identities, tokens, accounts, rse_settings, and rules, and may allow modification of database contents. The issue affects Oracle deployments using the default json_meta plugin and does not affect PostgreSQL or MySQL deployments using that plugin. This vulnerability has been fixed in versions 35.8.5, 38.5.5, 39.4.2, and 40.1.1.
- 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.