CVE-2025-14009: NLTK Downloader RCE Vulnerability

CVE-2025-14009 Overview

A critical remote code execution vulnerability exists in the NLTK (Natural Language Toolkit) downloader component, affecting all versions of the nltk/nltk library. The vulnerability resides in the _unzip_iter function within nltk/downloader.py, which uses zipfile.extractall() without performing path validation or security checks. This allows attackers to craft malicious zip packages that, when downloaded and extracted by NLTK, can execute arbitrary code on the target system.

Critical Impact

This vulnerability enables full system compromise through remote code execution. Attackers can achieve file system access, network access, and establish persistence mechanisms by exploiting NLTK's implicit trust in downloaded packages.

Affected Products

• NLTK (Natural Language Toolkit) - All versions
• Python applications using nltk.download() functionality
• Systems with NLTK configured to download external data packages

Discovery Timeline

• 2026-02-18 - CVE-2025-14009 published to NVD
• 2026-02-19 - Last updated in NVD database

Technical Details for CVE-2025-14009

Vulnerability Analysis

This vulnerability is classified as Code Injection (CWE-94). The core issue stems from NLTK's design assumption that all downloaded packages are inherently trusted. When users invoke nltk.download() to retrieve language data packages, the library extracts zip archives without validating the contents or paths of the extracted files.

The _unzip_iter function directly calls zipfile.extractall(), which is known to be susceptible to path traversal and arbitrary file write attacks when processing untrusted archives. An attacker who can inject a malicious package into the download stream—or compromise a package repository—can include specially crafted files that will be automatically executed.

The attack achieves code execution through Python's import mechanism. When a malicious package contains Python files such as __init__.py, these files are automatically executed when the extracted package is imported. This creates a direct path from downloading seemingly benign NLP data to full remote code execution.

Root Cause

The root cause is the absence of input validation and security checks in the zip extraction process. The zipfile.extractall() method trusts the archive contents implicitly, allowing:

1. Path traversal attacks: Malicious archives can contain entries with relative paths (e.g., ../../) that write files outside the intended extraction directory
2. Automatic code execution: Python package structures with __init__.py files are executed automatically upon import
3. No integrity verification: Downloaded packages are not validated against known-good checksums or signatures before extraction

Attack Vector

The attack is network-based and requires no user interaction beyond initiating a package download. An attacker can exploit this vulnerability through several scenarios:

1. Man-in-the-middle attacks: Intercepting NLTK download requests and substituting malicious packages
2. Compromised package repositories: If an attacker gains access to NLTK data servers, they can replace legitimate packages with malicious ones
3. Supply chain attacks: Distributing applications or notebooks that automatically call nltk.download() with references to attacker-controlled packages

The exploitation mechanism involves crafting a zip archive containing a Python package structure with malicious code in the __init__.py file. When NLTK extracts and subsequently imports this package, the attacker's code executes with the privileges of the running Python process.

For detailed technical analysis of the vulnerability mechanism, see the Huntr Bounty Submission.

Detection Methods for CVE-2025-14009

Indicators of Compromise

• Unexpected Python processes spawning from NLTK data directories
• Unusual network connections originating from Python processes running NLTK
• New or modified files in NLTK data directories containing unexpected Python code
• Presence of __init__.py files in NLTK corpus or data directories where they should not exist

Detection Strategies

• Monitor file system activity in NLTK data directories (typically ~/nltk_data or system-wide locations) for creation of executable Python files
• Implement network monitoring for nltk.download() operations connecting to unexpected endpoints
• Use application-level logging to track all NLTK download operations and verify against expected package lists
• Deploy file integrity monitoring (FIM) on NLTK data directories to detect unauthorized modifications

Monitoring Recommendations

• Configure endpoint detection solutions to alert on Python script execution from NLTK data directories
• Establish baseline behavior for applications using NLTK and alert on anomalies in network or file system activity
• Review Python import statements and module loading for packages originating from NLTK data paths
• Implement egress filtering to restrict NLTK downloads to known-good repositories only

How to Mitigate CVE-2025-14009

Immediate Actions Required

• Audit all systems and applications using NLTK to identify exposure to the vulnerable download functionality
• Avoid using nltk.download() in production environments until a patch is available
• Pre-download and manually verify required NLTK data packages in isolated environments before deploying to production
• Implement network controls to restrict or monitor NLTK download operations

Patch Information

No official patch has been released at the time of this writing. Monitor the Huntr Bounty Submission and the official NLTK repository for updates on remediation status.

Organizations should consider implementing defense-in-depth measures until an official fix is available, including running NLTK workloads in sandboxed environments with restricted privileges.

Workarounds

• Download NLTK data packages manually from trusted sources and extract them using validated extraction utilities rather than relying on nltk.download()
• Run applications using NLTK in containerized environments with restricted file system access and network egress
• Implement application sandboxing to limit the impact of potential code execution
• Use network segmentation to isolate systems running NLTK from critical infrastructure

# Manual NLTK data installation workaround
# Download packages manually and verify integrity before extraction

# Create isolated NLTK data directory
mkdir -p /opt/nltk_data_verified

# Set NLTK to use the verified data directory
export NLTK_DATA=/opt/nltk_data_verified

# Manually download and verify packages before extraction
# Use checksums from trusted sources to validate package integrity