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GHSA-jv2h-4p9v-wf5w

Vulnerability in ouroboros-ai (GHSA-jv2h-4p9v-wf5w)

Summary

vulnerability in ouroboros-ai (GHSA-jv2h-4p9v-wf5w). Risk of unauthorized operations or information disclosure. Exploitable via ``_UNTRUSTED_ENV_DENYLIST``. Mitigation: upgrade to `0.42.1` or later.

AI summary snake-internal / snake-material-v2

A vulnerability tracked as **GHSA-jv2h-4p9v-wf5w** has been found in ouroboros-ai. Attackers can target a specific entry point like ``_UNTRUSTED_ENV_DENYLIST`` over the network to misuse the product. Risk of unauthorized operations or information disclosure. CVSS score: ?/10. What to do: upgrade ouroboros-ai to **0.42.1** or later. If unsure, ask your IT team or search "ouroboros-ai GHSA-jv2h-4p9v-wf5w" on the vendor's site.
GHSA-jv2h-4p9v-wf5w (ouroboros-ai) — CWE-15 / Attack surface: `_UNTRUSTED_ENV_DENYLIST` / `CODEX_HOME` / `OPENCODE_CONFIG` / `OPENCODE_CONFIG_DIR` Patched: `0.42.1` — apply immediately Workaround: Do not run Ouroboros from an untrusted/cloned repository directory; remove any project-directory `.env` and `./.ouroboros/mcp_servers.yaml` before running. 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
**A vulnerability** (CWE-15) exists in ouroboros-ai. Attackers reach the vulnerable code path via ``_UNTRUSTED_ENV_DENYLIST`` without authentication.
📍 Affected scope
ouroboros-ai — . Attack surface: `_UNTRUSTED_ENV_DENYLIST` / `CODEX_HOME` / `OPENCODE_CONFIG` / `OPENCODE_CONFIG_DIR`.
🔥 Severity
Severity: ?. Risk of unauthorized operations or information disclosure
🔧 How to fix
Update to **0.42.1**.
🛡️ Workaround
Workaround: Do not run Ouroboros from an untrusted/cloned repository directory; remove any project-directory `.env` and `./.ouroboros/mcp_servers.yaml` before running.
🔍 Detection
Search webserver/proxy logs for unusual request patterns matching this CVE's known IOCs. Run `grep -r 'ouroboros-ai' .` against your dependency files (package-lock.json, requirements.txt, go.sum) to find affected services.

Response Actions (7 steps)

Concrete steps and command examples for SOC/SRE teams to execute in order

  1. 1
    Identify exposure identify 
    grep -r 'ouroboros-ai' . | 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 `ouroboros-ai` を grep し、稼働しているサービス・バージョンを把握する。

  2. 5
    Apply temporary workaround mitigate 
    Do not run Ouroboros from an untrusted/cloned repository directory; remove any project-directory `.env` and `./.ouroboros/mcp_servers.yaml` before running.

    パッチが適用されるまでの応急処置として、Do not run Ouroboros from an untrusted/cloned repository directory; remove any project-directory `.env` and `./.ouroboros/mcp_servers.yaml` before running. を実施。回避策の副作用 (機能低下) を確認した上で。

  3. 6
    Apply patch patch 
    Upgrade ouroboros-ai to 0.42.1

    ステージング環境で 0.42.1 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と、Step 3 で見つけた異常検知の続報チェックを含めること。

  4. 7
    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 Step 3 と同じログクエリでアラート再発が無いか継続監視。

Affected packages

pip ouroboros-ai
[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"0.42.1"}]}]

References

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