CVE-2026-0848
Published: 05 March 2026
Summary
CVE-2026-0848 is a critical-severity Improper Input Validation (CWE-20) vulnerability in Nltk Nltk. Its CVSS base score is 10.0 (Critical).
Operationally, ranked in the top 48.9% 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 Supply Chain and Deployment risk domain.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-9875
Vulnerability details
NLTK versions <=3.9.2 are vulnerable to arbitrary code execution due to improper input validation in the StanfordSegmenter module. The module dynamically loads external Java .jar files without verification or sandboxing. An attacker can supply or replace the JAR file, enabling…
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the execution of arbitrary Java bytecode at import time. This vulnerability can be exploited through methods such as model poisoning, MITM attacks, or dependency poisoning, leading to remote code execution. The issue arises from the direct execution of the JAR file via subprocess with unvalidated classpath input, allowing malicious classes to execute when loaded by the JVM.
- 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: model poisoning, nltk
Related Threats
CVEs Like This One
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.
Directly implements checks on information inputs to reject invalid data before processing.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.