CVE-2026-79657 in NLTK
Summary
by MITRE • 08/25/2026
NLTK versions before 3.10.3 contain a remote code execution vulnerability in allowlisted pickle loaders that trust entire module namespaces instead of specific safe callables. Attackers can craft malicious pickle payloads invoking dangerous in-namespace functions like ReppTokenizer._execute and numpy.f2py.crackfortran.myeval through pickle REDUCE to execute arbitrary commands during model or tokenizer artifact loading.
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Analysis
by VulDB Data Team • 08/25/2026
The vulnerability identified in Natural Language Toolkit (NLTK) versions prior to 3.10.3 represents a critical security flaw rooted in the improper deserialization of Python objects using the pickle module. This issue specifically affects allowlisted pickle loaders that were designed to mitigate risks by restricting access to specific safe functions, yet they fail to enforce this restriction at the namespace level. Instead of validating individual callable targets against a strict whitelist, these loaders trust entire module namespaces. This architectural oversight allows attackers to bypass intended safety mechanisms by invoking dangerous functions within those trusted modules through standard pickle deserialization protocols. The core technical flaw lies in the reliance on broad namespace inclusion rather than granular function-level validation, which creates an attack surface where any public method or attribute within a loaded module can be executed if it is accessible via the pickle REDUCE opcode.
The operational impact of this vulnerability is severe, as it enables remote code execution (RCE) when processing untrusted model artifacts or tokenizer data. Attackers can craft malicious pickle payloads that exploit specific functions known to perform dangerous operations during their initialization or invocation phases. For instance, the ReppTokenizer._execute method and numpy.f2py.crackfortran.myeval are cited as examples of in-namespace functions that can be triggered through this mechanism. When a victim application loads a compromised model file or tokenizer configuration containing these crafted payloads, the pickle deserializer will execute the REDUCE command, leading to the invocation of these hazardous methods. This results in arbitrary code execution on the host system with the privileges of the user running the NLTK process, potentially allowing for complete system compromise, data exfiltration, or lateral movement within a networked environment.
From a classification perspective, this vulnerability aligns closely with CWE-502, which describes Deserialization of Untrusted Data. The specific mechanism involves trusting dangerous functions during deserialization, often categorized under CWE-471 if it pertains to modification assumptions, but primarily falls under the broader category of insecure default configurations regarding allowed modules and classes in serialization libraries. In terms of adversary tactics, this exploit maps directly to ATT&CK technique T1059, Command and Scripting Interpreter, specifically through Python execution capabilities. It also relates to T1203, Exploitation for Client Execution if the vulnerability is triggered via a client-side application loading models, or T1078, Valid Accounts, if exploited in conjunction with compromised credentials that allow access to model repositories. The attack vector typically involves tricking an administrator or automated pipeline into processing a maliciously crafted artifact, leveraging social engineering or supply chain compromise tactics.
Mitigation strategies must prioritize immediate updates and defensive coding practices. Organizations using NLTK should upgrade to version 3.10.3 or later, where the allowlist mechanism has been hardened to validate specific safe callables rather than entire namespaces. For environments that cannot immediately patch due to dependency constraints, it is crucial to implement strict input validation on all model files and tokenizer configurations before they are passed to NLTK loaders. Additionally, deploying runtime application self-protection (RASP) solutions or using sandboxed execution environments can limit the impact of successful exploitation by restricting file system access and network connectivity for processes handling untrusted data. Security teams should also audit their codebases for any custom pickle deserialization logic that might rely on similar namespace-trusting patterns and refactor them to use explicit whitelists of allowed classes and methods, ensuring that only verified safe operations are permitted during the loading process.