Categories
CVE-2026-39890 critical CVSS 9.8

Unsafe Deserialization in praisonai (CVE-2026-39890)

Summary

Vulnerability in praisonai (CVE-2026-39890). Successful exploitation can lead to full system takeover. Exploitable via AgentService.loadAgentFromFile. Mitigation: upgrade to 4.5.115 or later.

Original description

The description as published by the source.

Summary

The AgentService.loadAgentFromFile method uses the js-yaml library to parse YAML files without disabling dangerous tags (such as !!js/function and !!js/undefined). This allows an attacker to craft a malicious YAML file that, when parsed, executes arbitrary JavaScript code. An attacker can exploit this vulnerability by uploading a malicious agent definition file via the API endpoint, leading to remote code execution (RCE) on the server.

Details

The vulnerability exists in the YAML deserialization process. The js-yaml library's load function is used without specifying a safe schema (e.g., JSON_SCHEMA or DEFAULT_SAFE_SCHEMA). This enables the parsing of JavaScript functions and other dangerous types. When a malicious YAML file containing a !!js/function tag is parsed, the function is evaluated, leading to arbitrary code execution.

The vulnerable code is located in src/agents/agent.service.ts at line 55.

PoC

An attacker can create a malicious agent YAML file with the following content:

!!js/function >
  function(){ require('child_process').execSync('touch /tmp/pwned') }

Then, upload this file as an agent definition via the API endpoint that uses AgentService.loadAgentFromFile. When the agent is loaded (either during startup or via an API call that triggers loading), the payload will execute the command touch /tmp/pwned, demonstrating arbitrary code execution.

Impact

This vulnerability allows an unauthenticated attacker (…

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-2026-39890 has been found in praisonai.
Attackers can target a specific entry point like AgentService.loadAgentFromFile over the network to misuse the product.
Successful exploitation can lead to full system takeover. CVSS score: 9.8/10.
What to do: upgrade praisonai to 4.5.115 or later.
If unsure, ask your IT team or search "praisonai CVE-2026-39890" on the vendor's site.

CVE-2026-39890 (praisonai) — CWE-502 / CVSS v3 9.8
Attack vector: remote (network-reachable) / unauthenticated / no user interaction
Attack surface: AgentService.loadAgentFromFile / load / JSON_SCHEMA / DEFAULT_SAFE_SCHEMA
Patched: 4.5.115 — 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.

❓ What is the problem

Unsafe deserialization (CWE-502) exists in praisonai. Attackers reach the vulnerable code path via AgentService.loadAgentFromFile without authentication.

📍 Affected scope

Check the vendor advisory for the affected versions of praisonai. Attack surface: AgentService.loadAgentFromFile / load / JSON_SCHEMA / DEFAULT_SAFE_SCHEMA.

🔥 Severity

Severity: Critical (CVSS 9.8/10). Successful exploitation can lead to full system takeover.

🔧 How to fix

Update to 4.5.115.

🛡️ Workaround

Until the patch is applied: disable the affected feature, apply WAF rules, or restrict access via network ACLs.

🔍 Detection

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

  1. 1
    Identify 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。

  2. 2
    Apply patch patch
    Upgrade praisonai to 4.5.115

    ステージング環境で 4.5.115 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。

  3. 3
    Post-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

pip praisonai
[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"4.5.115"}]}]
PyPI praisonai
[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"4.5.115"}]}]

References

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