CVE-2026-62388 in NLTK
Summary
by MITRE • 08/22/2026
NLTK versions before 3.10.0 default to ENFORCE=False in pathsec.py, causing all security validation functions to emit warnings instead of raising exceptions. Attackers can bypass path traversal and pickle deserialization protections by exploiting the disabled security controls that are only active when manually enabled.
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Analysis
by VulDB Data Team • 08/22/2026
The vulnerability identified in Natural Language Toolkit (NLTK) versions prior to 3.10.0 stems from a critical misconfiguration within the library's internal security mechanisms, specifically located in the pathsec.py module. By defaulting the ENFORCE flag to False, the software fails to activate essential security validation functions that are designed to prevent unauthorized file system access and unsafe data deserialization. This configuration choice effectively neutralizes two major categories of application vulnerabilities: path traversal attacks and insecure pickle deserialization. When these protections are disabled by default, any code utilizing NLTK components for processing external inputs or loading serialized objects operates without the necessary safeguards against malicious payloads crafted to exploit filesystem boundaries or execute arbitrary code through Python's pickle module.
From a technical perspective, this flaw represents a failure in secure defaults and defense-in-depth principles. The path traversal protection is intended to ensure that file operations remain confined within expected directories, preventing attackers from reading sensitive system files such as /etc/passwd on Unix-like systems or accessing configuration data outside the application's scope. Similarly, pickle deserialization protections are crucial because Python's pickle module can instantiate arbitrary objects during unpickling if not properly restricted. By emitting warnings instead of raising exceptions when these checks fail, NLTK allows potentially dangerous operations to proceed silently from an execution standpoint, relying entirely on developer vigilance rather than enforcing security boundaries automatically. This approach violates the principle that secure defaults should be the norm for libraries intended for broad use across diverse application environments where developers may not possess deep expertise in every underlying security mechanism.
The operational impact of this vulnerability is significant, particularly for applications that process untrusted data or integrate with third-party models and datasets. An attacker who can influence file paths passed to NLTK functions could perform path traversal attacks to read arbitrary files on the host system, leading to information disclosure. More critically, if an application uses pickle to load model weights, tokenizers, or other serialized objects provided by external sources, it becomes susceptible to remote code execution (RCE). Since many machine learning pipelines rely heavily on NLTK for text preprocessing and feature extraction, this vulnerability can serve as a primary attack vector in cloud-native environments where models are frequently downloaded from public repositories. The lack of enforced exceptions means that even if security logs record warnings, the application continues its operation with compromised integrity, potentially allowing an attacker to gain full control over the underlying server or container environment.
This issue aligns closely with CWE-20 Improper Input Validation and CWE-501 Trust Boundary Violation, as it reflects a failure to validate external inputs against expected constraints before processing them. Furthermore, in the context of the MITRE ATT&CK framework, this vulnerability facilitates techniques related to Command and Control via Remote File Copy or Initial Access through Exploitation of Remote Services, depending on how NLTK is integrated into the broader application architecture. The ability to bypass deserialization checks directly maps to T1059 Code Execution, while path traversal capabilities relate to T1083 File and Directory Discovery. These mappings highlight that the vulnerability is not merely a configuration oversight but a fundamental weakness in the library's security posture that enables multiple stages of an attack chain.
To mitigate this risk, organizations using NLTK versions prior to 3.10.0 must immediately upgrade to version 3.10.0 or later, where these security controls are enforced by default and exceptions are raised upon violation rather than warnings being emitted. For environments that cannot yet upgrade due to compatibility constraints with legacy codebases, a mandatory workaround involves explicitly setting the ENFORCE flag to True in pathsec.py before any NLTK operations are performed. Additionally, developers should audit all instances of pickle usage within their applications, replacing them with safer serialization formats such as JSON or MessagePack where possible, and implementing strict allow-lists for file paths accessed by NLTK functions. Regular security assessments and static code analysis tools configured to detect insecure deserialization patterns can further help identify residual risks in existing deployments until the upgrade is fully implemented across all systems.