Code’s iMessage Plugin: The SMS Spoofing Vulnerability You Can’t Ignore + Video

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Introduction:

The integration of AI coding assistants with personal communication channels like iMessage introduces a dangerous trust boundary that many developers overlook. As AI agents gain the ability to act on external signals, any weakness in authentication at the integration layer—such as relying on spoofable phone numbers—can translate directly into unauthorized code execution, data breaches, or system compromise. This article explores the critical vulnerability discovered in Code’s iMessage plugin, where SMS spoofing allows attackers to send arbitrary commands to the AI agent, bypassing authentication and gaining full access to the user’s coding environment.

Learning Objectives:

  • Understand the security implications of using SMS/iMessage as an authentication channel for AI coding assistants
  • Identify the vulnerabilities associated with phone number spoofing and SQLite database exposure
  • Implement enterprise-grade mitigation strategies, including MCP server whitelisting and cryptographic verification

You Should Know:

  1. The iMessage SQLite Vulnerability: How Spoofed SMS Commands Bypass Authentication

The core issue stems from Code’s plugin architecture, which directly accesses the SQLite database containing iMessage conversations. As noted in the original post, “Anthropic literally accesses the SQLite db of your texts for read/write access.” This means that any message appearing in the Messages database is treated as a legitimate command, regardless of whether it originated from a verified iMessage contact or a spoofed SMS.

What makes this particularly dangerous is that SMS messages—unlike iMessages—can be spoofed with relative ease. Attackers can use services like Twilio, Telnyx, or less reputable SMS spoofing tools to send messages that appear to come from the victim’s own phone number or from a trusted contact. Once the message lands in the SQLite database, Code processes it as a valid command, potentially modifying code, deploying infrastructure changes, or exfiltrating sensitive data.

Step‑by‑step guide to inspecting and understanding this vulnerability:

  1. Locate the iMessage SQLite Database: On macOS, iMessage stores conversations in ~/Library/Messages/chat.db. This is a SQLite database that the Code plugin queries.

2. Query to inspect recent messages:

sqlite3 ~/Library/Messages/chat.db "SELECT text, is_from_me, date FROM message ORDER BY date DESC LIMIT 10;"

This command reveals that there’s no inherent field differentiating a spoofed SMS from a legitimate iMessage within the database structure itself—only the `is_from_me` flag indicates direction.

  1. Test SMS spoofing on your own number: Using a service like Twilio, send an SMS that appears to come from your own phone number: 
    from twilio.rest import Client
client = Client(account_sid, auth_token)
message = client.messages.create(
body="!modify deploy --environment production",
from_="+1234567890",  Your own number
to="+1234567890"  Same number, triggering iMessage on Mac
)

    This message will appear in the chat.db and trigger Code if the plugin is active.

  2. Monitor Code’s reaction: Check the Code logs (typically ~/.-code/logs/) to see if the command was executed. In the original test, the author confirmed “I was able to use a service to spoof my number to get Code to modify my website.”

  3. Extract command history: Attackers with write access to the SQLite database could also inject malicious commands retroactively. Use the following to see what commands have been processed:

    grep -r "executed command" ~/.-code/logs/

  4. Restricting MCP Servers: Enterprise Controls and API-Level Whitelisting

For enterprise deployments, the safer pattern is to scope allowed tools at the API level rather than relying on plugin-level controls. Valentin Charrier’s comment highlights this approach: “using Anthropic’s enterprise controls to whitelist only internal MCP servers with proper auth.” The Model Context Protocol (MCP) servers act as bridges between and external tools; restricting these at the API level prevents unauthorized channels from being used, even if a plugin like the iMessage connector is installed.

Step‑by‑step guide to implementing API-level whitelisting:

  1. Access Anthropic Enterprise Admin Console: Navigate to the organization settings and locate “MCP Server Management.”

  2. Disable all community/unverified MCP servers: Set default policy to “block all” and create an allowlist.

  3. Define allowed internal MCP servers: Only permit MCP servers that are hosted internally and require mutual TLS (mTLS) or API key authentication.

  4. Configure allowed tools per server: For each approved MCP server, specify which tools (e.g., filesystem, webhook, deploy) can be invoked by .

  5. Implement network restrictions: Ensure that MCP servers are not exposed to the public internet. Use firewall rules to restrict inbound connections to only Anthropic’s API IP ranges or your VPN/CIDR blocks.

  6. Audit existing plugin usage: Run this command on developer endpoints to check for unauthorized plugins:

    macOS: Check for presence of the iMessage plugin
find ~/Library/Application\ Support/ -name "imessage" -o -name "sms" 2>/dev/null

    For Linux (if using Code via WSL or native), check:

    find ~/.config/ ~/. -name "imessage" -o -name "sms" 2>/dev/null

  7. Enforce via MDM: For organizations using mobile device management (MDM), push a configuration that prevents installation of unofficial plugins, or forces the use of a managed configuration profile that overrides plugin settings.

  8. The SMS vs. iMessage Authentication Gap: Cryptographic Verification

Jacob Turpin’s comment introduces a potential mitigation: “I believe this can be mitigated using iMessage Contact Key Verification.” iMessage Contact Key Verification (CKV) is a feature that cryptographically verifies that the recipient of an iMessage is indeed who you intend to communicate with, preventing man-in-the-middle (MITM) attacks. However, this only applies to true iMessages (blue bubbles) routed through Apple’s servers, not SMS (green bubbles). The plugin’s fundamental mistake is treating SMS and iMessage as equivalent trust sources.

Step‑by‑step guide to enforcing cryptographic verification for iMessage channels:

  1. Enable Contact Key Verification on all devices: On macOS, go to Messages > Preferences > iMessage, and ensure “Verify Contact Keys” is enabled. This requires all devices to be updated to macOS 15+ and iOS 18+.

  2. Verify contacts before trusting iMessage commands: Before allowing any iMessage-triggered action, the plugin should programmatically check if the sender is CKV-verified. This requires accessing the Contacts framework on macOS:

    // Sample Objective-C check (conceptual)
CNContactStore store = [[CNContactStore alloc] init];
NSArray keys = @[CNContactIdentifierKey, CNContactVerifiedKeysKey];
CNContact contact = [store unifiedContactWithIdentifier:contactId keysToFetch:keys error:nil];
if (contact.verifiedKeys.count == 0) {
// Sender not verified, reject command
}

  3. For developers building their own adapters: If you’re creating an SMS/iMessage adapter (as Tom di Mino did with his Telnyx/Twilio adapter), ensure that you implement cryptographic signing. The recommended approach is to generate a per-user API key that must be included in the command message, and use HMAC to verify authenticity:

    import hmac
import hashlib</p></li>
</ol>

<p>def verify_command(sender_number, command_text, secret_key):
expected_signature = hmac.new(
secret_key.encode(),
f"{sender_number}:{command_text}".encode(),
hashlib.sha256
).hexdigest()

Extract signature from command, e.g., "!deploy:signature"
command_parts = command_text.split(":")
if len(command_parts) < 2 or command_parts[bash] != expected_signature:
return False
return True
    1. Log all unverified command attempts: For security monitoring, create an alert for any SMS messages that attempt to issue commands without proper cryptographic authentication.

    4. Building a Secure SMS Adapter: Step-by-Step Implementation

    Tom di Mino’s GitHub repository provides a template for creating a secure SMS adapter: https://github.com/tdimino/claudicle/blob/main/docs/channel-adapters.md. Rather than directly accessing the iMessage SQLite database, a secure adapter should use a controlled channel like Twilio with number verification, webhook authentication, and rate limiting.

    Step‑by‑step guide to implementing a secure SMS-based command channel:

    1. Set up a Twilio number with webhook: Configure a Twilio phone number to forward incoming messages to your own webhook endpoint. This gives you control over verification before any command reaches .

    2. Implement sender verification: Before passing any message to , verify that the sender’s number is in an allowlist. Use a secure store (like a database with encrypted entries) for allowed numbers:

      from twilio.twiml.messaging_response import MessagingResponse
from flask import Flask, request</p></li>
</ol>

<p>app = Flask(<strong>name</strong>)

@app.route("/sms", methods=['POST'])
def sms_reply():
sender = request.form['From']
message_body = request.form['Body']

Check if sender is in allowlist
if not is_verified_number(sender):
resp = MessagingResponse()
resp.message("Unauthorized sender. Command not executed.")
return str(resp)

Forward verified message to via secure API
response = _api.execute_command(message_body, authenticated_user=get_user_from_number(sender))
return str(response)
      1. Add command signing: If you need to support automated systems, implement command signing where the message must contain a valid signature derived from a shared secret and the sender’s number.

      2. Rate limit and throttle: Prevent brute-force or denial-of-service attempts by implementing rate limiting per phone number:

        from functools import wraps
from time import time</p></li>
</ol>

<p>rate_limits = {}

def rate_limit(limit=5, per_seconds=60):
def decorator(f):
@wraps(f)
def wrapper(args, kwargs):
sender = request.form['From']
now = time()
window_start = now - per_seconds

Clean old entries
rate_limits[bash] = [t for t in rate_limits.get(sender, []) if t > window_start]

if len(rate_limits.get(sender, [])) >= limit:
return "Rate limit exceeded", 429

rate_limits.setdefault(sender, []).append(now)
return f(args, kwargs)
return wrapper
return decorator
        1. Audit logs: Log all command attempts with timestamps, sender numbers, and command content (redacted if necessary) to a centralized SIEM for threat detection.

        5. Enterprise Policy Hardening: Proactive Controls

        The original post emphasizes the need for enterprise controls: “And if you’re an enterprise, hopefully you have some way of controlling what plugins your developers are using.” Organizations must move beyond trusting individual developers to manage their own AI tool security and implement centralized governance.

        Step‑by‑step guide to hardening enterprise AI tool usage:

        1. Create a Software Bill of Materials (SBOM) for AI tools: Inventory all AI assistants, plugins, and MCP servers in use across development teams.

        2. Enforce configuration management: Use tools like Chef, Puppet, or Ansible to push approved Code configurations that explicitly disable high-risk plugins:

          Example ansible task to disable iMessage plugin</p></li>
</ol>

<p>- name: Disable Code iMessage plugin
file:
path: ~/Library/Application Support//plugins/imessage
state: absent
become: no
when: ansible_os_family == "Darwin"
          1. Network isolation for AI agents: Run Code and other AI assistants in isolated VMs or containers with minimal network access. Restrict outbound connections to only Anthropic API endpoints and necessary internal services.

          2. Implement DLP (Data Loss Prevention): Monitor for sensitive data being sent to or any MCP server. Use egress filtering and data classification to block commands containing PII, credentials, or proprietary code.

          3. Regular security assessments: Conduct quarterly reviews of AI tool configurations, including testing for SMS/phone number spoofing vulnerabilities as demonstrated in the original post. Red teams should attempt to exploit these channels to validate controls.

          4. Developer security training: Educate developers on the risks of connecting AI agents to insecure channels. Include specific examples of spoofing attacks and the importance of cryptographic verification over simple identifiers.

          What Undercode Say:

          • SMS is not authentication: Phone numbers and SMS should never be treated as reliable authentication factors. The ease of spoofing makes them dangerous for authorizing actions in AI systems.
          • AI agents inherit trust boundaries: A vulnerability in any integrated channel becomes a vulnerability in the AI agent itself. Enterprises must treat AI toolchains with the same security rigor as production infrastructure.

          The revelation that Code’s iMessage plugin exposes users to trivial SMS spoofing attacks is a stark reminder that security in the AI era requires rethinking trust models. As David Hague noted, “AI removes complexity, so AI retroactively removes the protection” previously afforded by obscurity. This vulnerability is not an isolated incident—it signals a broader trend where the convenience of AI agents interacting with legacy communication channels introduces dangerous attack surfaces. Organizations must immediately audit their AI toolchains, enforce API-level controls, and mandate cryptographic verification for any external channel that can trigger code execution. The industry’s rapid adoption of AI agents demands equally rapid evolution of security practices, moving from trusting identifiers to verifying identities.

          Prediction:

          The exploitation of AI agent communication channels—whether SMS, email, or webhooks—will become a primary attack vector in 2026-2027. We will see a rise in “AI jacking” where attackers target the integration layers of autonomous agents to perform unauthorized actions. This will drive the development of new security frameworks specifically for AI agent tooling, including mandatory cryptographic signing of all external inputs and standardized MCP server authentication protocols. Organizations that fail to secure these channels will face significant breaches as attackers shift focus from traditional infrastructure to AI-powered developer tools.

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