CVE-2026-72818
HighCVSS 7.5Exploitation Probability (EPSS)
Elevated risk53th percentile - higher than 53% of all known CVEs
Summary
The URLS regular expression in nltk/tokenize/casual.py, used by TweetTokenizer, contains an unbounded domain branch causing catastrophic backtracking on malicious input. A few kilobytes of text can consume seconds to minutes of CPU, enabling unauthenticated DoS. Version 3.10.1 bounds the label repetition.
Risk Assessment
Services using TweetTokenizer or casual_tokenize on untrusted text can be stalled per request, leading to resource exhaustion and service disruption.
Recommendation
Upgrade NLTK to version 3.10.1 or later, which includes a fix that bounds label repetition in the regular expression.
Other vulnerabilities in NLTK
See all- CVE-2026-109956High
NLTK through 3.10.3 contains a path traversal vulnerability in WekaClassifier that allows attackers to bypass nltk.pathsec containment by supplying unvalidated model paths. Attackers controlling the model_filename or model argument can make the weka JVM write files via -d or probe files via -l outside allowed data roots.
- CVE-2026-79675Critical
NLTK before 3.10.3 fails to validate JVM options passed through the per-call options parameter in the java() function, allowing attackers to inject dangerous JVM flags. Attackers can supply malicious options like -agentpath, -javaagent, or @argfile to Stanford wrapper classes to achieve arbitrary code execution.
- CVE-2026-79657Critical
NLTK versions before 3.10.3 contain a remote code execution vulnerability in allowlisted pickle loaders that trust entire module namespaces instead of specific safe callables. Attackers can craft malicious pickle payloads invoking dangerous in-namespace functions like ReppTokenizer._execute and numpy.f2py.crackfortran.myeval through pickle REDUCE to execute arbitrary commands during model or tokenizer artifact loading.
- CVE-2026-78683Critical
NLTK before 3.10.0 contains an unsafe pickle deserialization vulnerability in TransitionParser.parse(). Deserialization uses default restricted=False, allowing arbitrary Python code execution when loading a crafted model file.
- CVE-2026-81727High
NLTK versions before 3.10.3 contain a filesystem containment bypass vulnerability in the Downloader.download and Downloader.incr_download methods that allows attackers to overwrite files outside the install root through pre-existing hardlinks. Attackers with write access to a shared downloader directory can create hardlinks pointing to outside-root files that are then overwritten during normal package extraction.
- CVE-2026-81726High
NLTK through 3.10.3 contains a path traversal vulnerability in model-artifact APIs that bypass pathsec enforcement by using raw file operations on caller-controlled paths. Attackers can read or write files outside allowed sandbox roots through TransitionParser, AveragedPerceptron, PerceptronTagger, and maxent parameter APIs when pathsec is enabled.
- CVE-2026-81725Low
NLTK before 3.10.3 has a regular expression denial of service vulnerability in Pl196xCorpusReader. Attackers can supply malformed TEI blocks causing quadratic CPU consumption.
- CVE-2026-81724Medium
NLTK before 3.10.3 contains an uncontrolled recursion vulnerability in nltk.featstruct.FeatStructReader that allows unauthenticated attackers to cause a denial of service by supplying deeply nested feature-structure input. Attackers can craft trivial payloads with nested brackets that exceed Python's recursion limit and raise an unhandled RecursionError, crashing applications that parse user-supplied feature structures or feature grammars.
- CVE-2026-81723Low
NLTK before 3.10.3 has a quadratic CPU exhaustion vulnerability in XMLCorpusView._read_xml_fragment(). Malformed XML files can cause severe CPU consumption and DoS.
- CVE-2026-81722High
NLTK PorterStemmer in versions <= 3.10.2 (fixed in 3.10.3) contains an inefficient-algorithmic-complexity denial of service in PorterStemmer.stem(). The _is_consonant() helper walks backward over the entire run of trailing 'y' characters on every call, and _measure() invokes it for each stem position, causing O(n^2) behavior. A single ~20-50 KB untrusted token consisting of a long run of the letter 'y' followed by a matching suffix (e.g., 'ness') can pin a CPU core for seconds to minutes.
Original NVD description (English source)
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.
Vulnerability data from NVD (NIST) · CISA KEV · EPSS

