What is the CVSS score of CVE-2021-43854?

CVE-2021-43854 has a CVSS 3.1 base score of 7.5 (High). CVSS vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H. EPSS exploitation probability: 2.7%.

Is there a public PoC exploit available for CVE-2021-43854?

Yes, a public proof-of-concept exploit is available for CVE-2021-43854. Prioritise patching immediately. EPSS: 2.7%.

Is there a patch available for CVE-2021-43854?

Yes, a patch is available for CVE-2021-43854. Update the affected software to the latest version immediately.

Python CVE-2021-43854

HIGH

Uncontrolled Resource Consumption (CWE-400)

2021-12-23 security-advisories@github.com

Python Denial Of Service Nltk

7.5

CVSS 3.1 · Vendor: github

Severity by source

Vendor (github) PRIMARY

7.5 HIGH

AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Primary rating from Vendor (github) · only source for this CVE.

CVSS VectorVendor: github

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Attack Vector

Network

Attack Complexity

Low

Privileges Required

None

User Interaction

None

Scope

Unchanged

Confidentiality

None

Integrity

None

Availability

High

Lifecycle Timeline

CVE Published

Dec 23, 2021 - 18:15 cve.org

HIGH 7.5

Blast Radius

ecosystem impact

† from your stack dependencies † transitive graph · vuln.today resolves 4-path depth

16 pypi packages depend on nltk (15 direct, 1 indirect)

Ecosystem-wide dependent count for version 3.6.6.

DescriptionCVE.org

NLTK (Natural Language Toolkit) is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. Versions prior to 3.6.5 are vulnerable to regular expression denial of service (ReDoS) attacks. The vulnerability is present in PunktSentenceTokenizer, sent_tokenize and word_tokenize. Any users of this class, or these two functions, are vulnerable to the 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. 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. For users unable to upgrade 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.

AnalysisAI

NLTK (Natural Language Toolkit) is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.

Technical ContextAI

This vulnerability is classified as Uncontrolled Resource Consumption (CWE-400), which allows attackers to cause denial of service by exhausting system resources. NLTK (Natural Language Toolkit) is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. Versions prior to 3.6.5 are vulnerable to regular expression denial of service (ReDoS) attacks. The vulnerability is present in PunktSentenceTokenizer, sent_tokenize and word_tokenize. Any users of this class, or these two functions, are vulnerable to the 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. 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. For users unable to upgrade 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. Affected products include: Nltk. Version information: prior to 3.6.5.

RemediationAI

A vendor patch is available. Apply the latest security update as soon as possible. Implement rate limiting, set resource quotas, validate input sizes, use timeouts.