Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/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-2026-12044 is a high-severity SQL Injection (CWE-89) vulnerability in Pgadmin Pgadmin 4. Its CVSS base score is 8.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Content Injection (T1659); ranked in the top 50% of CVEs by exploit likelihood; 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-2026-37963
Vulnerability Data
SQL injection in pgAdmin 4 across every dialog template that renders ``COMMENT ON ... IS '<description>'`` for a user-supplied description field. The Jinja templates for Domains (and their constraints), Foreign Tables, Languages, and Event Triggers, plus the Views OID-lookup query,…
more
interpolated the description directly inside a single-quoted SQL literal -- ``'{{ data.description }}'`` -- instead of passing it through the ``qtLiteral`` escape filter. An authenticated pgAdmin user with permission to create or alter the affected object types could submit a description containing an apostrophe, break out of the literal and chain arbitrary SQL. The injected SQL runs under the PostgreSQL role the user is already authenticated as; for a connected role with ``COPY ... TO/FROM PROGRAM`` (typically PostgreSQL superuser), this chains to OS command execution on the PostgreSQL host. The defect does not cross a privilege boundary -- the user already has direct SQL access to that role through pgAdmin's Query Tool -- so the attacker gains no capability beyond what their database role already grants. The marginal impact captures bypass of any application-layer Query Tool gating an operator may have configured. The defect was originally reported against the Domain Dialog ``description`` field; a code-wide audit identified sixteen sites of the same pattern across the templates listed above. The same review also surfaced ten related sinks in the pgstattuple/pgstatindex stats templates -- ``pgstattuple('{{schema}}.{{table}}')`` and the matching pgstatindex shape -- where ``qtIdent`` escapes embedded double quotes inside the identifier but not apostrophes, so a user with CREATE privilege on a schema could plant a table or index named ``foo'bar`` and a later stats viewer would render an unbalanced literal. Fix is layered: 1. Sites: replace every ``'{{ x.description }}'`` with ``{{ x.description|qtLiteral(conn) }}`` (no surrounding quotes -- the filter wraps the value in escaped quotes itself). Plumb ``conn=self.conn`` through every ``render_template`` call that loads one of these templates. Also corrects a ``{ % elif`` Jinja typo in the foreign-table schema diff (dead branch). Rewrite the ten pgstattuple/pgstatindex stats sites to address the relation via OID + ``::oid::regclass`` cast (e.g. ``pgstattuple({{ tid }}::oid::regclass)``), eliminating the embedded literal-call form entirely so that bug-class can no longer recur there. 2. Driver hardening: ``qtLiteral`` (in ``utils/driver/psycopg3/__init__.py``) used to silently return the raw unescaped value when its ``conn`` argument was falsy. It now raises ``ValueError`` -- surfacing the entire bug class going forward. The change immediately uncovered eight latent plumbing bugs (in ``schemas/__init__.py``, ``schemas/functions/__init__.py``, ``schemas/tables/utils.py``, ``foreign_servers/__init__.py``, and seven sites in ``roles/__init__.py``) -- all fixed as part of this patch. The inner ``except`` block that swallowed adapter-level failures and returned the raw value is also removed, so unadaptable inputs raise instead of leaking unescaped values. 3. Regression tests: a per-template behavioural test renders each previously-vulnerable template with an apostrophe-injection payload and asserts the escaped fragment is present and the vulnerable fragment absent; a lint test walks every ``*.sql`` template flagging any ``'{{ ... }}'`` single-quote-wrapped interpolation against an explicit allowlist; unit tests cover the new qtLiteral fail-fast and inner-except raise paths. This issue affects pgAdmin 4: from 1.0 before 9.16.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
—
V1.1.2V1.2.1V1.2.3V6.2.5
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.
Secure coding standards explicitly require correct output encoding and escaping to preserve message structure.
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.
Application security requirements include explicit rules for safe output handling and encoding.