CyberRota Analysis
AI-GeneratedThe vulnerability affects the TweetTokenizer in the NLTK library, where an unbounded regular expression can lead to excessive CPU consumption when processing specially crafted input. This can result in denial-of-service conditions for services that utilize the tokenizer on untrusted social media text, allowing attackers to stall requests without authentication. Organizations using NLTK for text processing should prioritize patching to version 3.10.1 or later to mitigate this risk.
Public Exploit Signal
A public exploit, PoC, GitHub repository or Metasploit reference was detected for this CVE.
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Original NVD Description
The URLS regular expression in nltk/tokenize/casual.py, compiled into TweetTokenizer.WORD_RE and applied by TweetTokenizer.tokenize, contains a naked-domain branch whose domain-label prefix [a-z0-9]+(?:[.\-][a-z0-9]+)* is unbounded. Input consisting of many alternating label separators can be partitioned in exponentially many ways, and because the branch also requires a trailing top-level domain that such input never supplies, the engine explores those partitions before failing at each offset. A few kilobytes of input therefore consumes seconds to minutes of single-threaded CPU, and the HANG_RE substitution performed before matching does not collapse the pattern. TweetTokenizer is intended for tokenizing untrusted social-media text, so any service that applies it, or the module-level casual_tokenize, to submitted text can be stalled per request without authentication. Version 3.10.1 bounds the label repetition.