CVE-2026-81722: NLTK Denial of Service Vulnerability

MODERATE
Advisory Identifier: GHSA-ww6m-cw3f-q94g
CWE-407
Published
Advisory Summary

CVE-2026-81722 is a MODERATE denial of service vulnerability (CWE-407: Inefficient Algorithmic Complexity) affecting NLTK libraries in the PyPI ecosystem. The issue involves quadratic-time dos in porterstemmer via long runs of 'y'. Remediation is available by upgrading affected packages to version 3.10.3.

CVE-2026-81722 at a Glance

CVE Identifier CVE-2026-81722
GHSA Identifier GHSA-ww6m-cw3f-q94g
Severity MODERATE
CVSS v3.1 Base Score N/A
CWE Classification CWE-407: Inefficient Algorithmic Complexity
Affected Product NLTK
Package Ecosystem PyPI
Published Date
Last Updated

How to Fix CVE-2026-81722

To remediate this vulnerability, upgrade all affected NLTK dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.

nltk fixed: 3.10.3
pip install nltk>=3.10.3
Verify Direct and Transitive Dependencies

Run the following command in your project directory to verify that all resolved dependencies use the patched release:

pip show nltk

How to Check if You Are Affected

To determine whether your Python environment is affected by CVE-2026-81722, inspect your installed packages using pip:

pip show nltk

Analyze the terminal output to locate any version of NLTK in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 3.10.3), your application is affected.

Note: The pip show nltk command checks currently installed packages in your active Python environment.

Technical Details

Vulnerability Mechanism & Technical Analysis

This vulnerability is classified under CWE-407: Inefficient Algorithmic Complexity, where an algorithm in a product has an inefficient worst-case computational complexity that may be detrimental to system performance and can be triggered by an attacker, typically using crafted manipulations that ensure that the worst case is being reached. In NLTK, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.

Source Advisory Statement

`nltk.stem.PorterStemmer.stem()` -- a ubiquitous public API applied to arbitrary, often untrusted, tokens -- runs in O(n^2) time on a token containing a long run of the letter 'y', letting a single ~20-50 KB token pin a CPU core (CWE-407). ## Root cause `_is_consonant(word, i)` was made *iterative* (commit for #3633, GHSA/CWE-674) to fix an earlier unbounded-recursion `RecursionError` on `'y'*10000`. The iterative form walks *backward* over the whole run of 'y's on every call: ```python while i > 0 and word[i] == 'y': negate = not negate i -= 1 ``` `_measure()` then calls `_is_consonant(stem, i)` once for **every** position `i` of the stem. For a run of n 'y's that is sum_{i} O(i) = O(n^2). The recursion fix therefore traded a CWE-674 RecursionError for a CWE-407 quadratic-time DoS. ## Proof of concept Measured (Python 3.13): `stem('y'*5000 + 'ness')` = 2.6s, `stem('y'*10000 + 'ness')` = 11.3s (2x input -> ~4.3x time = quadratic), `stem('y'*20000 + 'ness')` > 20s. A pure run of 'y' with no matching suffix is fast because the stemmer rules that call `_measure` do not fire; a real suffix such as 'ness' triggers `_measure` on the long stem. ```python from nltk.stem import PorterStemmer PorterStemmer().stem('y' * 20000 + 'ness') # >20s of CPU ``` ## Impact Stemming is routinely applied to untrusted text (search, indexing, NLP pipelines). A single unbroken ~20-50 KB token of 'y' characters (no whitespace, so it survives tokenization) causes multi-second-to-minutes CPU consumption per request. No confidentiality/integrity impact; single-process availability only. ## Fix direction Classify each character's consonant/vowel status in a single left-to-right O(n) pass (memoise the 'y' run parity) instead of re-walking the run on every `_is_consonant` call, so `_measure` and stemming are linear. This is a sibling of the corpus-reader quadratic advisories GHSA-vp2x-qp44-57v7 and GHSA-8mpw-7fpc-4gqj (CWE-407).

CVE-2026-81722 Affected NLTK Versions

1 component(s)
Fixed Versions:
3.10.3
Product / Package Ecosystem Affected Versions Fixed Version
nltk
PyPI
< 3.10.3
3.10.3

References

Frequently Asked Questions

What is CVE-2026-81722?
CVE-2026-81722 is a security vulnerability in NLTK involving Quadratic-time DoS in PorterStemmer via long runs of 'y' under CWE-407: Inefficient Algorithmic Complexity.
Which NLTK versions are affected?
Affected versions include releases prior to 3.10.3 across affected packages: nltk.
What version fixes CVE-2026-81722?
Upgrading to 3.10.3 (or the latest patched release for your respective branch) resolves the vulnerability.
Is nltk affected?
Yes, nltk is affected in releases prior to 3.10.3. Upgrade your dependency to 3.10.3 or later.
What CWE is associated with CVE-2026-81722?
CVE-2026-81722 is classified under CWE-407: Inefficient Algorithmic Complexity (An algorithm in a product has an inefficient worst-case computational complexity that may be detrimental to system performance and can be triggered by an attacker, typically using crafted manipulations that ensure that the worst case is being reached.).
How can I check PyPI dependencies for CVE-2026-81722?
Run the dependency tree command: 'pip show nltk'. This command lists direct and transitive dependencies in your build configuration so you can verify if a vulnerable version is resolved.
Machine-readable OSV JSON OSV v1.5 Schema
Normalized schema output
{
  "schema_version" : "1.4.0",
  "id" : "GHSA-ww6m-cw3f-q94g",
  "modified" : "2026-09-02T14:36:15Z",
  "published" : "2026-09-02T14:36:15Z",
  "aliases" : [ "CVE-2026-81722" ],
  "summary" : "NLTK: Quadratic-time DoS in PorterStemmer via long runs of 'y'",
  "details" : "`nltk.stem.PorterStemmer.stem()` -- a ubiquitous public API applied to arbitrary, often untrusted, tokens -- runs in O(n^2) time on a token containing a long run of the letter 'y', letting a single ~20-50 KB token pin a CPU core (CWE-407).\n\n## Root cause\n\n`_is_consonant(word, i)` was made *iterative* (commit for #3633, GHSA/CWE-674) to fix an earlier unbounded-recursion `RecursionError` on `'y'*10000`. The iterative form walks *backward* over the whole run of 'y's on every call:\n\n```python\nwhile i > 0 and word[i] == 'y':\n    negate = not negate\n    i -= 1\n```\n\n`_measure()` then calls `_is_consonant(stem, i)` once for **every** position `i` of the stem. For a run of n 'y's that is sum_{i} O(i) = O(n^2). The recursion fix therefore traded a CWE-674 RecursionError for a CWE-407 quadratic-time DoS.\n\n## Proof of concept\n\nMeasured (Python 3.13): `stem('y'*5000 + 'ness')` = 2.6s, `stem('y'*10000 + 'ness')` = 11.3s (2x input -> ~4.3x time = quadratic), `stem('y'*20000 + 'ness')` > 20s. A pure run of 'y' with no matching suffix is fast because the stemmer rules that call `_measure` do not fire; a real suffix such as 'ness' triggers `_measure` on the long stem.\n\n```python\nfrom nltk.stem import PorterStemmer\nPorterStemmer().stem('y' * 20000 + 'ness')   # >20s of CPU\n```\n\n## Impact\n\nStemming is routinely applied to untrusted text (search, indexing, NLP pipelines). A single unbroken ~20-50 KB token of 'y' characters (no whitespace, so it survives tokenization) causes multi-second-to-minutes CPU consumption per request. No confidentiality/integrity impact; single-process availability only.\n\n## Fix direction\n\nClassify each character's consonant/vowel status in a single left-to-right O(n) pass (memoise the 'y' run parity) instead of re-walking the run on every `_is_consonant` call, so `_measure` and stemming are linear. This is a sibling of the corpus-reader quadratic advisories GHSA-vp2x-qp44-57v7 and GHSA-8mpw-7fpc-4gqj (CWE-407).",
  "severity" : [ {
    "type" : "CVSS_V4",
    "score" : "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N"
  } ],
  "affected" : [ {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "nltk"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "0"
      }, {
        "fixed" : "3.10.3"
      } ]
    } ],
    "database_specific" : {
      "last_known_affected_version_range" : "<= 3.10.2"
    }
  } ],
  "references" : [ {
    "type" : "WEB",
    "url" : "https://github.com/nltk/nltk/security/advisories/GHSA-ww6m-cw3f-q94g"
  }, {
    "type" : "ADVISORY",
    "url" : "https://nvd.nist.gov/vuln/detail/CVE-2026-81722"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/nltk/nltk/commit/7808692d451b962711005d954859bb83aabcf8fa"
  }, {
    "type" : "PACKAGE",
    "url" : "https://github.com/nltk/nltk"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/nltk/nltk/releases/tag/v3.10.3"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/pypa/advisory-database/tree/main/vulns/nltk/PYSEC-2026-3738.yaml"
  }, {
    "type" : "WEB",
    "url" : "https://www.vulncheck.com/advisories/nltk-porterstemmer-before-3.10.3-quadratic-time-dos"
  } ],
  "database_specific" : {
    "cwe_ids" : [ "CWE-407" ],
    "severity" : "MODERATE",
    "github_reviewed" : true,
    "github_reviewed_at" : "2026-09-02T14:36:15Z",
    "nvd_published_at" : null
  }
}