SQL Injection in praisonai (CVE-2026-60090)
Summary
SQL injection in praisonai (CVE-2026-60090). Successful exploitation can lead to full system takeover. Mitigation: upgrade to 4.6.78 or later.
Original description
The description as published by the source.
PraisonAI before 4.6.78 fails to validate the caller-controlled dimension argument in the PGVector and Cassandra knowledge-store create_collection() backends. Although schema, keyspace, and collection-name identifiers are validated, the dimension value (declared as int but not enforced at runtime) is interpolated directly into the vector column of the generated CREATE TABLE DDL. A caller able to influence collection-creation dimensions can pass a string such as '3); DROP TABLE tenant_secrets; --' to inject SQL/CQL tokens into the statement executed by the database driver.
Source: NIST National Vulnerability Database NVD GitHub Advisory
Auto-generated summary
Assembled by SecPulse from the source data (CVSS, CWE, affected versions) using templates — no AI involved. Check the original description and references below for exact details.
A vulnerability tracked as CVE-2026-60090 has been found in praisonai.
Successful exploitation can lead to full system takeover. CVSS score: 9.8/10.
What to do: upgrade praisonai to 4.6.78 or later.
If unsure, ask your IT team or search "praisonai CVE-2026-60090" on the vendor's site.
CVE-2026-60090 (praisonai) — CWE-89 / CVSS v3 9.8
Attack vector: remote (network-reachable) / unauthenticated / no user interaction
Patched: 4.6.78 — apply immediately
Plan: 1) Audit SBOM/dependencies, 2) Stage→prod upgrade, 3) Add WAF/proxy monitoring on affected endpoints, 4) Hunt IOCs in logs.
Refs: see the GHSA / vendor advisory / patched release linked on this page.
SQL injection (CWE-89) exists in praisonai.
Check the vendor advisory for the affected versions of praisonai.
Severity: Critical (CVSS 9.8/10). Successful exploitation can lead to full system takeover.
Update to 4.6.78.
Until the patch is applied: disable the affected feature, apply WAF rules, or restrict access via network ACLs.
Search webserver/proxy logs for unusual request patterns matching this CVE's known IOCs. Run grep -r 'praisonai' . against your dependency files (package-lock.json, requirements.txt, go.sum) to find affected services.
Response actions
Concrete steps and command examples for SOC/SRE teams to execute in order
-
1Identify exposure identify
grep -r 'praisonai' . | grep -v node_modulesリポジトリと本番環境の依存ファイル (package-lock.json / requirements.txt / go.sum / Gemfile.lock 等) で
praisonaiを grep し、稼働しているサービス・バージョンを把握する。 -
2Apply patch patch
Upgrade praisonai to 4.6.78ステージング環境で 4.6.78 に上げて回帰テスト → 本番反映。回帰テストはアプリの主要ハッピーパスを含めること。
-
3Post-deployment verification verify
Confirm patched version is live in productionパッチ適用後、ステージングで PoC または同等の悪用パターンを再現して脆弱性が閉じたことを確認。本番ではアクセスログ・WAF でアラート再発が無いか継続監視。
Affected packages
References
- advisory github.com
- advisory vulncheck.com
- advisory nvd.nist.gov
- advisory github.com
- advisory github.com
- package github.com
- package pypi.org
- patch github.com
- web github.com