Vulnerability in PraisonAI (CVE-2026-56077)
Summary
Vulnerability in PraisonAI (CVE-2026-56077). Confidential information can be exposed externally.
Original description
The description as published by the source.
PraisonAI before 1.5.115 contains an information disclosure vulnerability in the MultiAgentLedger component that allows attackers to access sensitive data by registering agents with duplicate IDs. Attackers can exploit the lack of agent ID uniqueness enforcement to share ledger instances and expose system prompts and conversation history between agents.
Source: GitHub Security Advisories (Global) 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-2026-56077 has been found in PraisonAI.
Confidential information can be exposed externally. CVSS score: 6.5/10.
What to do: apply the vendor's official patched release.
If unsure, ask your IT team or search "PraisonAI CVE-2026-56077" on the vendor's site.
CVE-2026-56077 (PraisonAI) — CWE-668 / CVSS v3 6.5
Attack vector: remote (network-reachable) / no user interaction
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.
A vulnerability (CWE-668) exists in PraisonAI.
Check the vendor advisory for the affected versions of PraisonAI.
Severity: Medium (CVSS 6.5/10). Confidential information can be exposed externally.
Update to the patched release per vendor advisory.
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。 -
2Post-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。
References
- web disclosure@vulncheck.com
- web nvd.nist.gov
- web disclosure@vulncheck.com
- web disclosure@vulncheck.com
- web github.com