CVE-2025-71408
Nltk ≤ 3.9.3
Raw vector
CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2025-71408 is a high-severity Eval Injection (CWE-95) vulnerability in Nltk Nltk. Its CVSS base score is 8.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked at the 17th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-210509
Vulnerability Data
NLTK (Natural Language Toolkit) before version 3.9.3 contains an eval injection vulnerability in the nltk.collocations module that allows an attacker who controls command-line arguments to execute arbitrary Python code. When collocations.py is invoked directly, the __main__ block passes command-line arguments…
more
directly to eval() as suffixes of BigramAssocMeasures without allowlist validation or sanitization, enabling an attacker to supply a Python expression that escapes the intended attribute lookup and executes arbitrary code including OS commands via the os module.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.2
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and code analysis can discover eval-injection flaws but does not stop their introduction.
Input validation explicitly requires neutralizing untrusted data before it reaches dynamic evaluation constructs such as eval.
Secure-development standards and tools can mandate safe coding patterns that avoid unsafe dynamic evaluation.
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.
Secure SDLC practices directly require input neutralization and avoidance of unsafe dynamic evaluation.
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.