Update dependency nltk to v3.9.3 [SECURITY]

[renovate]

This PR contains the following updates:

Package	Change	Age	Confidence
nltk (source)	==3.4.5 → ==3.9.3

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GitHub Vulnerability Alerts

CVE-2021-43854

Impact

The vulnerability is present in PunktSentenceTokenizer, sent_tokenize and word_tokenize. Any users of this class, or these two functions, are vulnerable to a Regular Expression Denial of Service (ReDoS) attack.
In short, a specifically crafted long input to any of these vulnerable functions will cause them to take a significant amount of execution time. The effect of this vulnerability is noticeable with the following example:

from nltk.tokenize import word_tokenize

n = 8
for length in [10**i for i in range(2, n)]:
    # Prepare a malicious input
    text = "a" * length
    start_t = time.time()
    # Call `word_tokenize` and naively measure the execution time
    word_tokenize(text)
    print(f"A length of {length:<{n}} takes {time.time() - start_t:.4f}s")

Which gave the following output during testing:

A length of 100      takes 0.0060s
A length of 1000     takes 0.0060s
A length of 10000    takes 0.6320s
A length of 100000   takes 56.3322s
...

I canceled the execution of the program after running it for several hours.

If your program relies on any of the vulnerable functions for tokenizing unpredictable user input, then we would strongly recommend upgrading to a version of NLTK without the vulnerability, or applying the workaround described below.

Patches

The problem has been patched in NLTK 3.6.6. After the fix, running the above program gives the following result:

A length of 100      takes 0.0070s
A length of 1000     takes 0.0010s
A length of 10000    takes 0.0060s
A length of 100000   takes 0.0400s
A length of 1000000  takes 0.3520s
A length of 10000000 takes 3.4641s

This output shows a linear relationship in execution time versus input length, which is desirable for regular expressions.
We recommend updating to NLTK 3.6.6+ if possible.

Workarounds

The execution time of the vulnerable functions is exponential to the length of a malicious input. With other words, the execution time can be bounded by limiting the maximum length of an input to any of the vulnerable functions. Our recommendation is to implement such a limit.

References

• The issue showcasing the vulnerability: https://github.com/nltk/nltk/issues/2866
• The pull request containing considerably more information on the vulnerability, and the fix: https://github.com/nltk/nltk/pull/2869
• The commit containing the fix: 1405aad979c6b8080dbbc8e0858f89b2e3690341
• Information on CWE-1333: Inefficient Regular Expression Complexity: https://cwe.mitre.org/data/definitions/1333.html

For more information

If you have any questions or comments about this advisory:

• Open an issue in github.com/nltk/nltk
• Email us at nltk.team@gmail.com

CVE-2021-3842

NLTK is vulnerable to REDoS in some RegexpTaggers used in the functions get_pos_tagger and malt_regex_tagger.

CVE-2021-3828

The nltk package is vulnerable to ReDoS (regular expression denial of service). An attacker that is able to provide as an input to the [_read_comparison_block()(https://github.com/nltk/nltk/blob/23f4b1c4b4006b0cb3ec278e801029557cec4e82/nltk/corpus/reader/comparative_sents.py#L259) function in the file nltk/corpus/reader/comparative_sents.py may cause an application to consume an excessive amount of CPU.

CVE-2024-39705

NLTK through 3.8.1 allows remote code execution if untrusted packages have pickled Python code, and the integrated data package download functionality is used. This affects, for example, averaged_perceptron_tagger and punkt.

CVE-2025-14009

A critical vulnerability exists in the NLTK downloader component of nltk/nltk, affecting all versions. The _unzip_iter function in nltk/downloader.py uses zipfile.extractall() without performing path validation or security checks. This allows attackers to craft malicious zip packages that, when downloaded and extracted by NLTK, can execute arbitrary code. The vulnerability arises because NLTK assumes all downloaded packages are trusted and extracts them without validation. If a malicious package contains Python files, such as init.py, these files are executed automatically upon import, leading to remote code execution. This issue can result in full system compromise, including file system access, network access, and potential persistence mechanisms.

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Release Notes

nltk/nltk (nltk)

v3.9.3

Compare Source

v3.9.2

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v3.9.1

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v3.9

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v3.8.1

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v3.8

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v3.7

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v3.6.7

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v3.6.6

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v3.6.5

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v3.6.4

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v3.6.3

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v3.6.2

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v3.6.1

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v3.6

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v3.5

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[sonarqubecloud]

Kudos, SonarCloud Quality Gate passed!   

0 Bugs
0 Vulnerabilities
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0 Code Smells

No Coverage information
No Duplication information

[sonarqubecloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

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