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NLTK: Pl196xCorpusReader has quadratic ReDoS on malformed TEI blocks

Moderate severity GitHub Reviewed Published Aug 12, 2026 in nltk/nltk • Updated Sep 8, 2026

Package

pip nltk (pip)

Affected versions

<= 3.10.2

Patched versions

3.10.3

Description

Summary

Pl196xCorpusReader still parses whole TEI blocks with multiple lazy regexes over attacker-controlled text. A malformed file with many opening tags and no matching closing tags forces repeated rescans and produces quadratic CPU growth in public reader APIs.

Details

  • Vulnerability type: Regular-expression denial of service
  • Affected component: nltk.corpus.reader.pl196x.TEICorpusView.read_block and Pl196xCorpusReader public methods
  • Affected versions: Published 3.9.4 and current source v3.10.0-rc2 both reproduced.
  • Patched versions: Not yet patched
  • Root cause: Lazy .*? whole-block regexes rescan untrusted XML-like blocks from each opening-tag position.

The parser uses regexes for paragraphs, sentences, and word tags across the whole <text> block. When the attacker supplies many unmatched opening tags, each attempt scans toward the end of the block and fails, then restarts from the next opening tag. There is near four-times runtime growth each time the number of malformed <p> tags doubled, through normal public calls such as words() and tagged_words().

PoC

Preconditions

  • The application parses attacker-influenced PL196X or TEI-like corpus files through public reader APIs.

Steps

  1. Create a corpus file with a valid header followed by a <text> block that contains many opening tags and no matching closing tags.
  2. Instantiate Pl196xCorpusReader on that corpus.
  3. Call words() or tagged_words() and measure elapsed time as the malformed tag count doubles.
  4. Observe near quadratic growth instead of near-linear behavior.

Minimal reproducible excerpt

size=1000 0.014s
size=2000 0.057s
size=4000 0.231s
size=8000 0.927s

Impact

A consumer that accepts attacker-influenced corpus files can be forced into heavy CPU use and parser-thread stalling before the application concludes the input contains no valid content.

Remediation

Replace the whole-block lazy-regex parser with a linear parser or bounded tokenizer, and add regression tests that assert near-linear behavior on malformed inputs with many unmatched tags.

References

@alvations alvations published to nltk/nltk Aug 12, 2026
Published to the GitHub Advisory Database Sep 8, 2026
Reviewed Sep 8, 2026
Last updated Sep 8, 2026

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity High
Attack Requirements None
Privileges Required None
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity None
Availability Low
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:H/AT:N/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(14th percentile)

Weaknesses

Uncontrolled Resource Consumption

The product does not properly control the allocation and maintenance of a limited resource. Learn more on MITRE.

Inefficient Regular Expression Complexity

The product uses a regular expression with an inefficient, possibly exponential worst-case computational complexity that consumes excessive CPU cycles. Learn more on MITRE.

CVE ID

CVE-2026-81725

GHSA ID

GHSA-8mpw-7fpc-4gqj

Source code

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