CVE-2026-80205 in NLTK
Summary
by MITRE • 08/26/2026
NLTK versions before 3.10.0 contain a regular expression denial of service vulnerability in Text.findall() and TokenSearcher.findall() methods that accept user-supplied regular expressions without validation or timeout. Attackers can supply crafted regex patterns that cause catastrophic backtracking, resulting in indefinite CPU saturation and denial of service to all users of the Python process.
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Analysis
by VulDB Data Team • 08/26/2026
The Natural Language Toolkit (NLTK) is a widely used library for symbolic and statistical natural language processing in Python, serving as a foundational resource for developers building applications involving text analysis, sentiment detection, and linguistic modeling. Within versions prior to 3.10.0, the Text.findall() and TokenSearcher.findall() methods present a critical security weakness by accepting user-supplied regular expressions without implementing any form of input validation or execution timeout mechanisms. This architectural oversight allows external actors to inject maliciously crafted regex patterns that exploit the inherent behavior of backtracking algorithms used in standard regular expression engines. When such patterns are processed, they trigger catastrophic backtracking, a phenomenon where the engine attempts an exponential number of paths to match the pattern against the input text, leading to indefinite CPU saturation and effectively causing a denial of service for all users sharing the Python process environment.
From a technical perspective, this vulnerability stems from the lack of safeguards when processing untrusted regular expressions in high-throughput or multi-user environments. Regular expression engines typically use backtracking to find matches; however certain patterns designed with nested quantifiers or ambiguous alternations can cause the engine to revisit states repeatedly without making progress toward a match. In the context of NLTK, since these methods do not enforce time limits on regex execution, an attacker can craft inputs that force the CPU to remain at one hundred percent utilization for extended periods. This results in resource exhaustion, preventing legitimate requests from being processed and degrading or halting service availability entirely. The impact is particularly severe in web applications or server-side services where NLTK is integrated into request handling pipelines, as a single malicious input can compromise the stability of the entire application instance.
This vulnerability aligns with Common Weakness Enumeration (CWE) category CWE-400, which describes Uncontrolled Resource Consumption, specifically manifesting here through CPU exhaustion via algorithmic complexity attacks. It also maps to MITRE ATT&CK technique T1496, known as Resource Hijacking, where an adversary uses computing resources for their own purposes or causes denial of service by consuming system capacity. The exploitation vector is typically remote if the vulnerable methods are exposed through a network-facing interface that accepts user input directly into text processing functions without sanitization.
To mitigate this risk, organizations should immediately upgrade to NLTK version 3.10.0 or later, where these specific regular expression handling flaws have been addressed with appropriate safeguards such as execution timeouts and stricter validation protocols. For environments unable to update promptly, developers must implement defensive coding practices by wrapping regex operations in timeout mechanisms that terminate processing if it exceeds a predefined duration threshold. Additionally, input validation should be enforced to restrict the types of patterns allowed, avoiding complex nested structures where possible. Monitoring CPU usage spikes associated with text processing modules can also serve as an early detection mechanism for such attacks, allowing administrators to isolate affected processes before they impact broader system stability.