Categories
CVE-2026-71416 high CVSS 8.8

Authentication Bypass in headroom-ai (CVE-2026-71416)

Summary

Authentication bypass in headroom-ai (CVE-2026-71416). Successful exploitation can lead to full system takeover. Exploitable via Authorization header. Mitigation: upgrade to 0.35.0 or later.

Original description

The description as published by the source.

Summary

The Headroom WebSocket server does not validate the Origin header of incoming client WebSocket requests before forwarding the request to the upstream server, allowing malicious WebSocket clients to perform arbitrary LLM requests without authentication. This can be exploited by a malicious WebSocket client executed in a traditional or headless browser such as lightpanda, if the browser has access to the Headroom proxy and the OpenAI API key is stored in the OPENAI_API_KEY environment variable.

Details

The Headroom server defines a WebSocket handler at ws://:8787/v1/responses in headroom/providers/proxy_routes.py:

    @app.websocket("/v1/responses")
    async def openai_responses_ws(websocket: WebSocket):
        await proxy.handle_openai_responses_ws(websocket)

In the handle_openai_responses_ws() method of the OpenAIHandlerMixin class, the Origin header of the WebSocket client handshake is not checked or verified before calling websocket.accept(), which grants any WebSocket client (including malicious clients) access to the server:

    async def handle_openai_responses_ws(self, websocket: WebSocket) -> None:
        """WebSocket proxy for /v1/responses (Codex gpt-5.4+).

        Newer Codex versions use WebSocket instead of HTTP POST for the
        Responses API.  This handler:
        1. Accepts the client WebSocket
        2. Receives the first message (``response.create`` request)
        3. Opens an upstream WebSock…

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-2026-71416 has been found in headroom-ai.
Attackers can target a specific entry point like Authorization header over the network to misuse the product.
Successful exploitation can lead to full system takeover. CVSS score: 8.8/10.
What to do: upgrade headroom-ai to 0.35.0 or later.
If unsure, ask your IT team or search "headroom-ai CVE-2026-71416" on the vendor's site.

CVE-2026-71416 (headroom-ai) — CWE-287 / CVSS v3 8.8
Attack vector: remote (network-reachable) / unauthenticated
Attack surface: Authorization header / Origin / OPENAI_API_KEY / OpenAIHandlerMixin
Patched: 0.35.0 — 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

Authentication bypass (CWE-287) exists in headroom-ai. Attackers reach the vulnerable code path via Authorization header without authentication.

📍 Affected scope

Check the vendor advisory for the affected versions of headroom-ai. Attack surface: Authorization header / Origin / OPENAI_API_KEY / OpenAIHandlerMixin.

🔥 Severity

Severity: High (CVSS 8.8/10). Successful exploitation can lead to full system takeover.

🔧 How to fix

Update to 0.35.0.

🛡️ 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 'headroom-ai' . 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 'headroom-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 headroom-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. 2
    Apply patch patch
    Upgrade headroom-ai to 0.35.0

    ステージング環境で 0.35.0 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 headroom-ai
[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"0.35.0"}]}]
PyPI headroom-ai
[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"0.35.0"}]}]

References

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