NLTK up to 3.10.2 Pickle Loader numpy.f2py.crackfortran numpy.f2py.crackfortran.myeval deserialization
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 8.4 | $0-$5k | 0.32 |
Summary
A vulnerability identified as critical has been detected in NLTK up to 3.10.2. This impacts the function numpy.f2py.crackfortran.myeval of the file numpy.f2py.crackfortran of the component Pickle Loader. Performing a manipulation results in deserialization.
This vulnerability is cataloged as CVE-2026-79657. It is possible to initiate the attack remotely. There is no exploit available.
Details
A vulnerability classified as critical has been found in NLTK up to 3.10.2. This affects the function numpy.f2py.crackfortran.myeval of the file numpy.f2py.crackfortran of the component Pickle Loader. The manipulation with an unknown input leads to a deserialization vulnerability. CWE is classifying the issue as CWE-502. The product deserializes untrusted data without sufficiently verifying that the resulting data will be valid. This is going to have an impact on confidentiality, integrity, and availability. The summary by CVE is:
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.
It is possible to read the advisory at github.com. This vulnerability is uniquely identified as CVE-2026-79657 since 08/25/2026. The exploitability is told to be easy. It is possible to initiate the attack remotely. No form of authentication is needed for exploitation. Technical details of the vulnerability are known, but there is no available exploit.
Upgrading to version 3.10.3 eliminates this vulnerability.
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
Product
Name
Version
Website
- Product: https://github.com/nltk/nltk/
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔒VulDB Reliability: 🔍
CNA CVSS-B Score: 🔒
CNA CVSS-BT Score: 🔒
CNA Vector: 🔒
CVSSv3
VulDB Meta Base Score: 8.5VulDB Meta Temp Score: 8.4
VulDB Base Score: 7.3
VulDB Temp Score: 7.0
VulDB Vector: 🔒
VulDB Reliability: 🔍
CNA Base Score: 9.8
CNA Vector (VulnCheck): 🔒
CVSSv2
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| Vector | Complexity | Authentication | Confidentiality | Integrity | Availability |
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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍
Exploiting
Class: DeserializationCWE: CWE-502 / CWE-20
CAPEC: 🔒
ATT&CK: 🔒
Physical: No
Local: No
Remote: Yes
Availability: 🔒
Status: Not defined
Price Prediction: 🔍
Current Price Estimation: 🔒
| 0-Day | Unlock | Unlock | Unlock | Unlock |
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Threat Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: UpgradeStatus: 🔍
0-Day Time: 🔒
Upgrade: NLTK 3.10.3
Timeline
08/25/2026 Advisory disclosed08/25/2026 CVE reserved
08/25/2026 VulDB entry created
08/25/2026 VulDB entry last update
Sources
Product: github.comAdvisory: github.com
Status: Confirmed
CVE: CVE-2026-79657 (🔒)
GCVE (CVE): GCVE-0-2026-79657
GCVE (VulDB): GCVE-100-395019
Entry
Created: 08/25/2026 13:54Changes: 08/25/2026 13:54 (77)
Complete: 🔍
Cache ID: 216::103
Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.
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