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CVE-2026-44716 high CVSS 7.5

Path Traversal in pipecat-ai (CVE-2026-44716)

Summary

Path traversal in pipecat-ai (CVE-2026-44716). Confidential information can be exposed externally. Exploitable via GET /files/{filename. Mitigation: upgrade to 1.2.0 or later.

Original description

The description as published by the source.

Summary

A path traversal vulnerability exists in Pipecat's development runner (src/pipecat/runner/run.py). When the runner is started with the --folder flag, it exposes a GET /files/{filename:path} download endpoint. The filename path parameter is concatenated directly onto args.folder with no containment check. Starlette normalises literal ../ sequences in URLs, but %2F-encoded slashes bypass this normalisation: the path parameter is URL-decoded after routing, so ..%2F..%2Fetc%2Fpasswd resolves to a path two levels above args.folder. An attacker with network access to the runner can read any file the pipecat process has permission to access — including SSH private keys, credentials, and system files — with a single unauthenticated HTTP request.

Confirmed on pipecat-ai 1.1.0 (latest PyPI release) and commit f078df78058ae82a02ce5b23e9e3a99a0917a53d.

Details

The vulnerable code is in src/pipecat/runner/run.py, inside the _configure_server_app() function, lines 249–264:

@app.get("/files/{filename:path}")
async def download_file(filename: str):
    """Handle file downloads."""
    if not args.folder:
        logger.warning(f"Attempting to dowload {filename}, but downloads folder not setup.")
        return

    file_path = Path(args.folder) / filename          # ← no containment check
    if not os.path.exists(file_path):
        raise HTTPException(404)

    media_type, _ = mimetypes.guess_type(file_path)

    return FileRe…

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-2026-44716 has been found in pipecat-ai.
Attackers can target a specific entry point like GET /files/{filename over the network to misuse the product.
Confidential information can be exposed externally. CVSS score: 7.5/10.
What to do: upgrade pipecat-ai to 1.2.0 or later.
If unsure, ask your IT team or search "pipecat-ai CVE-2026-44716" on the vendor's site.

CVE-2026-44716 (pipecat-ai) — CWE-22 / CVSS v3 7.5
Attack vector: remote (network-reachable) / unauthenticated / no user interaction
Attack surface: GET /files/{filename / GET /files/.. / GET /files/recording.txt / GET /files/../../etc/passwd
Patched: 1.2.0 — 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

Path traversal (CWE-22) exists in pipecat-ai. Attackers reach the vulnerable code path via GET /files/{filename without authentication.

📍 Affected scope

Check the vendor advisory for the affected versions of pipecat-ai. Attack surface: GET /files/{filename / GET /files/.. / GET /files/recording.txt / GET /files/../../etc/passwd.

🔥 Severity

Severity: High (CVSS 7.5/10). Confidential information can be exposed externally.

🔧 How to fix

Update to 1.2.0.

🛡️ 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 GET /files/{filename requests with malformed payloads or SQL meta-characters. Run grep -r 'pipecat-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 'pipecat-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 pipecat-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
    Hunt for indicators of compromise detect 
    grep 'GET /files/{filename' /var/log/nginx/access.log | grep -E '(unusual_payload|sqli_pattern)'

    アクセスログで GET /files/{filename への異常なリクエスト (不正な認証ヘッダ・SQLメタ文字)を過去 30〜90日分捜索。WAF/SIEM があれば該当パスのアラート発火履歴を確認。

  3. 3
    Apply patch patch 
    Upgrade pipecat-ai to 1.2.0

    ステージング環境で 1.2.0 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と、Step 2 で見つけた異常検知の続報チェックを含めること。

  4. 4
    Post-deployment verification verify 
    Replay attack against GET /files/{filename on staging to confirm patch closes the vector

    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 2 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 pipecat-ai
[{"type":"ECOSYSTEM","events":[{"introduced":"0"},{"fixed":"1.2.0"}]}]
PyPI pipecat-ai
[{"type":"ECOSYSTEM","events":[{"introduced":"0.0.90"},{"fixed":"1.2.0"}]}]

References

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