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SQL InjectionWeakness · CWE-89

CVE-2026-55405

HIGH · 7.6 CVSS v3.1 Published 2026-07-10
Patch available
A vendor patch is available. No clean upgrade release — apply the published patch.
See remediation →
82/100
Remediation priority · High
Remotely reachable Zero-click Patch available 6 weeks old

Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.

NVD · unedited
LangChain4j is a Java library for building LLM-powered applications on the JVM. Prior to 1.2.1-beta8, 1.5.1-beta11, 1.11.8-beta19, and 1.16.3-beta26, the MariaDB and pgvector embedding stores build metadata-filter SQL by string-concatenating filter keys, and in MariaDB string values, directly into the query without adequate escaping. A crafted metadata key in EmbeddingSearchRequest.filter() can break out of its SQL context and inject arbitrary SQL into the statements executed by the stores' search and removeAll(Filter) operations, enabling blind data exfiltration, denial of service via sleep functions, and deletion of arbitrary rows through removeAll(Filter). This issue is fixed in langchain4j-mariadb and langchain4j-pgvector versions 1.2.1-beta8, 1.5.1-beta11, 1.11.8-beta19, and 1.16.3-beta26.

Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.

dbcve analysis
How this class of weakness works · CWE-89

User input is woven into a database query, letting an attacker rewrite the query's logic. From there they can read, alter, or destroy data — frequently the entire database. The reliable fix is parameterised queries (prepared statements), so input is always treated as data and never as SQL.

General guidance for the sql injection class — the official description and references above are authoritative for this specific CVE. Want a bespoke review and a reviewed fix? Ask our team →

CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.

From the vector
Attack vector
Network
Complexity
Low
Privileges
Low
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
Low
Availability
Low

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:L

Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.

dbcve · scoped
Patch available Apply the vendor patch
Vendor patch github.com →
Recommended fix High confidence

langchain4j-mariadb and langchain4j-pgvector version 1.2.1-beta8 (or 1.5.1-beta11, 1.11.8-beta19, or 1.16.3-beta26)

  1. 1. Identify which LangChain4j embedding store module(s) are in use: langchain4j-mariadb, langchain4j-pgvector, or both. Check project dependencies (pom.xml, build.gradle, or equivalent).
  2. 2. Determine the current version of the affected dependency(ies) by examining the dependency management configuration.
  3. 3. Upgrade langchain4j-mariadb to version 1.2.1-beta8, 1.5.1-beta11, 1.11.8-beta19, or 1.16.3-beta26 (or later).
  4. 4. If using langchain4j-pgvector, upgrade it to version 1.2.1-beta8, 1.5.1-beta11, 1.11.8-beta19, or 1.16.3-beta26 (or later).
  5. 5. Rebuild and test the application to ensure the upgrade does not break existing functionality.
Caveat Beta versions may have API changes; review changelog before upgrading

Generated from the published advisory — verify against the referenced sources before acting.

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References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.

Primary sources

Practitioner notes

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