Vulnerabilities
Vulnerable Software
Nltk:  >> Nltk  >> 3.4.1  Security Vulnerabilities
NLTK before 3.10.0 (affected versions <= 3.9.4) contains a server-side request forgery (SSRF) vulnerability in the validate_network_url() function in nltk/pathsec.py. The _resolve_hostname() helper catches OSError and ValueError during socket.getaddrinfo() and returns an empty list; when DNS resolution fails, the validation loop executes no IP checks and the function fails open, allowing urlopen() to proceed without validation. An attacker who can trigger DNS resolution failures or use DNS rebinding can bypass SSRF protections and reach restricted network resources, including cloud metadata endpoints (e.g., 169.254.169.254).
CVSS Score
6.9
EPSS Score
0.002
Published
2026-08-22
NLTK versions before 3.9.4 contain an unbounded recursion vulnerability in JSONTaggedDecoder.decode_obj() that allows attackers to cause denial of service by supplying deeply nested JSON structures. Attackers can craft JSON payloads exceeding the recursion limit to trigger an unhandled RecursionError that crashes the Python process.
CVSS Score
8.7
EPSS Score
0.004
Published
2026-08-22
NLTK versions before 3.10.0 default to ENFORCE=False in pathsec.py, causing all security validation functions to emit warnings instead of raising exceptions. Attackers can bypass path traversal and pickle deserialization protections by exploiting the disabled security controls that are only active when manually enabled.
CVSS Score
8.7
EPSS Score
0.005
Published
2026-08-22
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 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.
CVSS Score
8.5
EPSS Score
0.002
Published
2026-07-24
In nltk/nltk versions 3.9.3 and earlier, five Stanford interface classes (StanfordPOSTagger, StanfordNERTagger, StanfordParser, StanfordDependencyParser, and StanfordNeuralDependencyParser) are vulnerable to untrusted JAR code execution. These classes accept user-controllable JAR paths and execute them via the `java()` function, which invokes `subprocess.Popen()` without integrity verification. This vulnerability is identical to CVE-2026-0848, which was fixed for StanfordSegmenter by adding SHA256 verification. However, the fix was not applied to these additional classes, leaving them susceptible to arbitrary code execution when loading untrusted JAR files.
CVSS Score
7.8
EPSS Score
0.002
Published
2026-07-04
NLTK (Natural Language Toolkit) is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. Prior to 3.10.0-rc1, nltk.data.load() in NLTK is vulnerable to path traversal via URL-encoded path separators and traversal segments when using the nltk: URL scheme. The unsafe-path regex check is performed before url2pathname() decodes the %xx sequences (a classic decode-after-check / TOCTOU-style flaw), allowing an attacker to bypass the protection documented in NLTK's SECURITY.md and read arbitrary files from the filesystem. While literal traversal strings such as ../../../etc/passwd are correctly blocked, encoded variants such as %2fetc%2fpasswd, %2e%2e%2f..., and ..%2f..%2f slip past the regex and are subsequently decoded into a real filesystem path. This vulnerability is fixed in 3.10.0-rc1.
CVSS Score
7.5
EPSS Score
0.006
Published
2026-06-22
NLTK (Natural Language Toolkit) is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. In versions 3.9.3 and prior, the NLTK downloader does not validate the `subdir` and `id` attributes when processing remote XML index files. Attackers can control a remote XML index server to provide malicious values containing path traversal sequences (such as `../`), which can lead to arbitrary directory creation, arbitrary file creation, and arbitrary file overwrite. Commit 89fe2ec2c6bae6e2e7a46dad65cc34231976ed8a patches the issue.
CVSS Score
8.1
EPSS Score
0.006
Published
2026-03-20
NLTK (Natural Language Toolkit) is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. In versions 3.9.3 and prior, `nltk.app.wordnet_app` contains a reflected cross-site scripting issue in the `lookup_...` route. A crafted `lookup_<payload>` URL can inject arbitrary HTML/JavaScript into the response page because attacker-controlled `word` data is reflected into HTML without escaping. This impacts users running the local WordNet Browser server and can lead to script execution in the browser origin of that application. Commit 1c3f799607eeb088cab2491dcf806ae83c29ad8f fixes the issue.
CVSS Score
6.1
EPSS Score
0.003
Published
2026-03-20
NLTK (Natural Language Toolkit) is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. In versions 3.9.3 and prior, `nltk.app.wordnet_app` allows unauthenticated remote shutdown of the local WordNet Browser HTTP server when it is started in its default mode. A simple `GET /SHUTDOWN%20THE%20SERVER` request causes the process to terminate immediately via `os._exit(0)`, resulting in a denial of service. Commit bbaae83db86a0f49e00f5b0db44a7254c268de9b patches the issue.
CVSS Score
7.5
EPSS Score
0.009
Published
2026-03-20
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 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.
CVSS Score
10.0
EPSS Score
0.008
Published
2026-03-05


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