Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:H/A:HSummary
CVE-2026-33236 is a high-severity Path Traversal (CWE-22) vulnerability in Nltk Nltk. Its CVSS base score is 8.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 44th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as NLP Libraries; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-33236 is a path traversal vulnerability (CWE-22) in the NLTK downloader component of the Natural Language Toolkit (NLTK), an open-source Python suite of modules, datasets, and tutorials for natural language processing research and development. In versions 3.9.3 and prior, the downloader does not validate the `subdir` and `id` attributes when processing remote XML index files, enabling attackers to inject path traversal sequences such as `../`.
An attacker controlling a remote XML index server exploited by a victim can supply malicious attribute values, resulting in arbitrary directory creation, arbitrary file creation, and arbitrary file overwrite on the victim's filesystem. Exploitation requires network access, low complexity, no privileges, and user interaction—such as invoking the NLTK downloader to fetch from the attacker's server—with a CVSS v3.1 base score of 8.1 (AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:H/A:H).
The vulnerability is addressed by commit 89fe2ec2c6bae6e2e7a46dad65cc34231976ed8a in the NLTK repository. Security advisories recommend updating to a patched version of NLTK and avoiding untrusted remote index servers; details are provided in the GitHub security advisory GHSA-469j-vmhf-r6v7.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-13887
Vulnerability Data
NLTK (Natural Language Toolkit) is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. In versions 3.9.3 and prior, the NLTK downloader does not validate the `subdir` and `id` attributes…
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when processing remote XML index files. Attackers can control a remote XML index server to provide malicious values containing path traversal sequences (such as `../`), which can lead to arbitrary directory creation, arbitrary file creation, and arbitrary file overwrite. Commit 89fe2ec2c6bae6e2e7a46dad65cc34231976ed8a patches the issue.
- CWE(s)
AI Security AnalysisAI
- AI Category
- NLP Libraries
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: nltk
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V5.3.2
Mitigating Controls (NIST 800-53 r5) AI
Enforces the intended directory access authorizations that path traversal would otherwise bypass.
Input validation directly neutralizes special path elements before pathname construction occurs.
Least privilege reduces the impact of any unauthorized file access obtained via traversal.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Patching/maintenance can remediate known path-traversal flaws in deployed software (partial prevention of exploitability) but does nothing to stop the coding defect from being introduced in the first place.
PR.AA-05 defines and reviews access policies but does not address code-level pathname neutralization, so neither direction prevents CWE-22.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing in development catches path traversal via static/dynamic analysis.
Secure SDLC mandates input validation and path sanitization that directly prevent path traversal.
Application security requirements include rules for safe file handling and canonicalization.
Secure architecture principles require least-privilege file access and directory isolation.
Secure coding standards explicitly forbid unsafe path construction and mandate safe APIs.
Information access restriction limits which files an application may read or write.