CVE-2026-123198
9.8 CRITICALpublic exploit availablePublished 2026-11-05 · Updated 2026-11-05
AI risk analysis
- Summary
- A server-side template injection (SSTI) flaw in LMFDB's render_knowl function allows unauthenticated attackers to inject and execute arbitrary Jinja2 expressions via request parameters, leading to full remote code execution as the web application user.
- Exploitability
- Exploitation is trivial for unauthenticated attackers due to lack of input sanitization and direct access to Python builtins through crafted query-string or POST data.
- Blast radius
- Successful exploitation grants full system compromise with the privileges of the web server process, enabling data theft, service disruption, or lateral movement within the infrastructure.
- Detection
- Look for HTTP requests to /knowledge/render/<ID> containing suspicious Jinja2 syntax (e.g., {{ config.__class__ }}, {{ ''.__class__.__mro__[1].__subclasses__() }}) or attempts to invoke Python builtins like __import__, eval, or os.system in query parameters or POST body; such patterns may appear in access logs or WAF alerts.
- Prioritized remediation
- Upgrade to LMFDB commit 01829bd or later, which patches the SSTI vulnerability by sanitizing user-supplied input before template rendering.
Analysis generated locally by garnet-coder (no data left the box). AI-assisted — verify against primary sources before acting.
NVD description
LMFDB before commit 01829bd contains a server-side template injection vulnerability in the render_knowl function that allows unauthenticated attackers to inject Jinja expressions via request parameters. Attackers can supply crafted query-string values or 'content' data to /knowledge/render/<ID> to reach Python builtins and execute operating system commands as the web application account.
CVSS vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
Weaknesses
CWE-1336
Public exploit & PoC references
- https://github.com/LMFDB/lmfdb/blob/c6e5bac205aa12de9edf584a06328fefa81de0de/lmfdb/knowledge/main.py#L747
- https://github.com/LMFDB/lmfdb/commit/01829bd09b692c720aad30c952758a20630573d4
- https://github.com/LMFDB/lmfdb/pull/7231
- https://github.com/LMFDB/lmfdb/security/advisories/GHSA-6jw3-pmcx-2xww
- https://github.com/LMFDB/lmfdb/security/advisories/GHSA-6jw3-pmcx-2xww
All references
- https://github.com/LMFDB/lmfdb/blob/c6e5bac205aa12de9edf584a06328fefa81de0de/lmfdb/knowledge/main.py#L747
- https://github.com/LMFDB/lmfdb/commit/01829bd09b692c720aad30c952758a20630573d4
- https://github.com/LMFDB/lmfdb/pull/7231
- https://github.com/LMFDB/lmfdb/security/advisories/GHSA-6jw3-pmcx-2xww
- https://www.vulncheck.com/advisories/lmfdb-before-commit-01829bd-unauthenticated-server-side-template-injection-via-knowledge-render-id
- https://github.com/LMFDB/lmfdb/security/advisories/GHSA-6jw3-pmcx-2xww
Source data: NVD (nvd.nist.gov), public domain. Exploit-DB.ai adds local AI analysis for defensive use only.
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