CVE-2026-79657: NLTK before 3.10.3 Remote Code Execution via Unsafe Pickle Deserialization
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.
Security readout for executives and security teams
Plain-English summary
NLTK before 3.10.3 can execute attacker-controlled commands when an application loads a malicious model or tokenizer artifact through affected pickle loaders. Successful exploitation could expose data, alter systems, or interrupt services without authentication or user interaction, according to the supplied CVSS assessment.
Executive priority
Prioritize immediate assessment and remediation for internet-facing, automated, or privileged services that load externally influenced NLTK artifacts. Upgrade broadly, but focus incident review on systems where attackers could modify or supply model files. Current sources do not establish active exploitation.
Technical view
The affected allowlist trusts whole Python module namespaces rather than individual safe callables. A crafted pickle can therefore reference dangerous functions within an allowed namespace through pickle deserialization, resulting in arbitrary command execution. The vulnerability is classified as CWE-502 and carries CVSS 3.1 score 9.8.
Likely exposure
Exposure is most likely where NLTK earlier than 3.10.3 loads pickle-based models or tokenizer artifacts obtained from, replaced by, or influenced by untrusted parties. Merely having NLTK installed does not establish an exploitable loading path; organizations must trace artifact sources and loader usage.
Exploitation context
The supplied bundle reports network-reachable, low-complexity exploitation requiring neither privileges nor user interaction. It does not identify CVE-2026-79657 as a CISA KEV entry or provide evidence of active exploitation. Treat practical exploit availability and observed attacks as unconfirmed.
Researcher notes
Key validation questions are whether an application invokes the affected allowlisted pickle loaders and whether an attacker can influence serialized input. The supplied description names ReppTokenizer._execute and numpy.f2py.crackfortran.myeval as examples, but complete affected loader coverage and exploitation telemetry are not provided.
Mitigation direction
Upgrade NLTK to version 3.10.3 or later.
Prevent applications from loading pickle artifacts from untrusted or mutable sources.
Restrict artifact repositories and verify artifact provenance and integrity.
Review the vendor advisory for deployment-specific guidance and backport information.
Validation and detection
Inventory deployed NLTK versions across production, development, containers, and build environments.
Identify code paths that load pickle-based models or tokenizer artifacts.
Trace each loaded artifact to its source, storage controls, and integrity checks.
Confirm upgraded environments report NLTK 3.10.3 or later.
Review logs for unexpected process execution associated with artifact-loading services.
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Potential ATT&CK relevance
Conservative CVE-to-ATT&CK context
These mappings and lookup hints may be relevant to the vulnerability behavior, CWE, affected product, or exposure path. Glexia-inferred context is not an official MITRE, ATT&CK, CWE, or CVE Program mapping.
ATT&CK lookup starting points
Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.
cwe · medium confidence lookup
CWE-502: Code execution behavior lookup
Code execution and unsafe deserialization weaknesses often justify reviewing execution behavior and process telemetry. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
The CVE wording references code or command execution, so execution technique review may help defensive triage. This is a Glexia inferred lookup path, not an official MITRE, ATT&CK, or CVE Program mapping.
These fields come from the CVE record and ADP containers, not from Glexia's Take. They preserve time-varying source decisions such as CISA SSVC, KEV status, CVSS metrics, and provider references.
2CVSS vectors
3Timeline events
1ADP providers
3Source links
SSVC decision data
CISA-ADPCISA Coordinator
Timestamp
Version
2.0.3
Exploitation: pocAutomatable: yesTechnical Impact: total
CVSS vector scores
2 official scores
We collect every scored CVSS vector available in the official CNA and ADP containers. When more than one version is present, the table keeps the source vectors side by side instead of collapsing them into the highest score.
CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.
CWE-502 · source CWE mapping
Deserialization of Untrusted Data
Deserialization of Untrusted Data represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.