Quick answer
Direct answer
Risk scores
| CVSS metric | What it means |
|---|---|
| Attack vector (AV:N) | Network: reachable remotely |
| Attack complexity (AC:L) | Low complexity |
| Attack requirements (AT:N) | No attack requirements |
| Privileges required (PR:N) | No privileges required |
| User interaction (UI:N) | No user interaction |
| Confidentiality (VC:H) | High confidentiality impact |
| Integrity (VI:N) | No integrity impact |
| Availability (VA:N) | No availability impact |
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X
About 1millionbot Millie Chatbot
CVE-2026-4399 affects 1millionbot Millie Chatbot, a product from 1millionbot. Before acting, confirm where 1millionbot Millie Chatbot runs in your estate, who owns it and whether it is reachable from the internet, because exposure decides real risk more than the score alone.
What the flaw is
In plain terms: Prompt injection vulnerability in 1millionbot Millie chatbot that occurs when a user manages to evade chat restrictions using Boolean prompt injection techniques (formulating a question in such a way that, upon receiving an affirmative response ('true'), the model executes the injected instruction), causing it to return prohibited information and information outside its intended context. Successful exploitation of this vulnerability could allow a malicious remote attacker to abuse the service for purposes other than those originally intended, or even execute out-of-context tasks using 1millionbot's resources and/or OpenAI's API key. This allows the attacker to evade the containment mechanisms implemented during LLM model training and obtain responses or chat behaviors that were originally restricted.
The weakness class is Command Injection. Untrusted input ends up inside a system command.
Based on its CVSS vector, the flaw can be reached over the network, needs no login, and needs no user interaction. Apply the vendor fix and confirm exposure on internet facing and business critical assets first.
Affected versions and timeline
| Field | Detail |
|---|---|
| Product | 1millionbot Millie Chatbot |
| Affected versions | Millie Chatbot before 3.6.0 |
| Published | Mar 31, 2026 |
| Last modified | Jun 17, 2026 |
| Added to CISA KEV | Not listed |
| Assigning CNA | cve-coordination@incibe.es |
How to detect it
- Inventory every instance of 1millionbot Millie Chatbot and record the running version against the affected range above.
- Check external attack surface scans for internet facing instances of the component.
- Review logs on affected systems for unusual requests, errors or new processes around the vulnerable function.
- Confirm with an authenticated vulnerability scan once your scanner has a check for this CVE.
How to fix it
- Apply the fixed version of 1millionbot Millie Chatbot named in the vendor advisory.
- If patching must wait, restrict network access to the affected service and put it behind authentication or a web application firewall rule.
- Rescan after the change to prove the exposure is gone, then close the ticket.
CTEM action plan
| CTEM stage | What to do for this CVE |
|---|---|
| Scope | Add every business service that depends on 1millionbot Millie Chatbot to the current CTEM cycle. |
| Discover | Find all instances, including shadow and cloud deployments, and record versions and exposure. |
| Prioritize | Rank using EPSS and CVSS together with asset exposure and business impact. |
| Validate | Confirm the flaw is reachable and exploitable in your environment and that compensating controls work. |
| Mobilize | Assign the fix to the asset owner with a due date matched to the priority, then verify closure. |
CWE References
- CWE-77: Command Injection More CVEs with this weakness
Technical References
Frequently asked questions
Related CVEs
Crafted MQTT messages can trigger command injection, resulting in root-level code execution on the target device.
A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the `_install_model_dependencies_to_env()` function. When deploying a model with `env_manager=LOCAL`, MLflow reads dependency specifications from the model artifact's `python_env.yaml` file and directly interpolates them into a shell command without sanitization. This allows an attacker to supply a malicious model artifact and achieve arbitrary command execution on systems that deploy the model. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.2.
This vulnerability allows authenticated attackers to execute arbitrary commands on the underlying system using the file name of an uploaded file.
This vulnerability allows authenticated attackers to execute commands via the hostname of the device.
Remote Code Execution vulnerability that allows unauthenticated attackers to inject arbitrary commands into the hostname of the device.
DocsGPT is a GPT-powered chat for documentation. From version 0.15.0 to before version 0.16.0, an attacker accessing both the official DocsGPT website or any local and public deployment, can craft a malicious payload bypassing the "MCP test" behavior to achieve arbitrary remote code execution (RCE). This issue has been patched in version 0.16.0.
Earn your free CTEM certification.
Learn to scope, prioritize, validate and mobilize fixes for CVEs like CVE-2026-4399 with a free CTEM certification.
Beginner / Practitioner / Program Leader
Explore certifications