Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2026-0847 is a high-severity Path Traversal (CWE-22) vulnerability in Nltk Nltk. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 44% of CVEs by exploit likelihood; 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 Privacy and Disclosure 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-2026-0847 is a path traversal vulnerability (CWE-22) in NLTK versions up to and including 3.9.2. It affects multiple CorpusReader classes, including WordListCorpusReader, TaggedCorpusReader, and BracketParseCorpusReader, which fail to properly sanitize or validate file paths. This enables attackers to traverse directories and access arbitrary files on the server where NLTK is deployed.
Remote unauthenticated attackers can exploit this vulnerability with low complexity, as indicated by its CVSS v3.1 score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N). Exploitation requires scenarios where user-controlled inputs are processed as file paths, such as in machine learning APIs, chatbots, or NLP pipelines. Successful attacks allow unauthorized reads of sensitive files, including system files, SSH private keys, and API tokens, and may escalate to remote code execution when combined with other vulnerabilities.
Mitigation details are available in the Huntr advisory at https://huntr.com/bounties/fc69914f-36a9-4c18-8503-10013b39f966.
This vulnerability is particularly critical for AI/ML and NLP applications relying on NLTK for corpus processing.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-9475
Vulnerability Data
A vulnerability in NLTK versions up to and including 3.9.2 allows arbitrary file read via path traversal in multiple CorpusReader classes, including WordListCorpusReader, TaggedCorpusReader, and BracketParseCorpusReader. These classes fail to properly sanitize or validate file paths, enabling attackers to traverse…
more
directories and access sensitive files on the server. This issue is particularly critical in scenarios where user-controlled file inputs are processed, such as in machine learning APIs, chatbots, or NLP pipelines. Exploitation of this vulnerability can lead to unauthorized access to sensitive files, including system files, SSH private keys, and API tokens, and may potentially escalate to remote code execution when combined with other vulnerabilities.
- CWE(s)
AI Security AnalysisAI
- AI Category
- NLP Libraries
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: machine learning, nltk
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
—
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.