PatchFirst · Latest vulnerabilities
Vulnerabilities published for nltk in the last 30 days: 1. Listed in the KEV catalogue: 0.
Vulnerabilities: 54
NLTK versions before 3.10.0 contain a logic bug in FileSystemPathPointer.open() where the sandbox validation check compares a normalized path against itself, making the security check permanently inert. Attackers can pass file:// URLs to nltk.data.load() to read arbitrary files accessible to the process user, including credentials and configuration files.
NLTK before 3.10.0 contains an arbitrary local file read vulnerability in StreamBackedCorpusView that bypasses pathsec.ENFORCE by calling builtins.open() directly instead of pathsec.open(). Attackers who control the fileid argument can read arbitrary local files regardless of the ENFORCE setting, including sensitive system files and application credentials.
NLTK before 3.10.0 (affected versions <= 3.9.4) contains a server-side request forgery (SSRF) vulnerability in the validate_network_url() function in nltk/pathsec.py. The _resolve_hostname() helper catches OSError and ValueError during socket.getaddrinfo() and returns an empty list; when DNS resolution fails, the validation loop executes no IP checks and the function fails open, allowing urlopen() to proceed without validation. An attacker who can trigger DNS resolution failures or use DNS rebinding can bypass SSRF protections and reach restricted network resources, including cloud metadata endpoints (e.g., 169.254.169.254).
NLTK versions before 3.10.0 default to ENFORCE=False in pathsec.py, causing all security validation functions to emit warnings instead of raising exceptions. Attackers can bypass path traversal and pickle deserialization protections by exploiting the disabled security controls that are only active when manually enabled.
NLTK versions before 3.10.0 contain a path traversal vulnerability in FramenetCorpusReader and NKJPCorpusReader that allows attackers to parse XML files outside the corpus root by supplying unsafe selectors or poisoned index state. Attackers can exploit frame_by_name, doc, lu, and header methods with crafted parameters to read arbitrary XML files accessible to the application.
NLTK versions before 3.10.2 contain a symlink-based sandbox bypass in FramenetCorpusReader that allows attackers to read arbitrary XML files outside the corpus root. Attackers can place symlinks with names containing no path separators inside the corpus subdirectory, which pass the path validation guard and are resolved to files outside the intended corpus root when accessed via frame_by_name(), _lu_file(), or doc() methods.
nltk versions before 3.10.2 contain a symlink-based arbitrary file read vulnerability in IPIPANCorpusReader methods that bypass nltk.pathsec validation entirely. Attackers can place a symlink in the corpus root directory and read arbitrary files accessible to the process by calling channels(), domains(), categories(), or fileids() methods with the symlink filename.
NLTK 3.9.4 through 3.10.2 contains a path traversal vulnerability in CrubadanCorpusReader. _load_lang_ngrams joins the corpus root with crubadan_code, the column-0 value read from the corpus table.txt mapping file, and opens the result with the builtin open() rather than the pathsec-validated opener, so os.path.join discards the root when that value is absolute and the read escapes the corpus directory without the containment check nltk.pathsec applies when ENFORCE is set. An attacker who controls a corpus package can disclose file contents outside the corpus root through lang_freq, limited to paths ending in -3grams.txt whose contents parse
NLTK before 3.10.3 contains a remote code execution vulnerability in AllowlistUnpickler that validates only the pickle module string and not the global name, allowing attackers to resolve dotted names by attribute traversal to callables outside the allowlisted namespace. Attackers can craft untrusted transition-parser models that execute arbitrary commands when TransitionParser.parse loads the model through allowlisted_pickle_load.
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
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A Server-Side Request Forgery (SSRF) vulnerability exists in nltk/nltk versions 3.9.4 and the current develop branch. The `nltk.pathsec.validate_network_url()` function, intended to prevent SSRF by rejecting internal network addresses, fails to reject IPs in the RFC 6598 shared address space (`100.64.0.0/10`). This occurs because Python's `ipaddress` module does not classify such addresses as `is_private` or `is_global`, and the current guard only checks `is_private` and a few explicit categories. An attacker who can influence a URL passed to NLTK's network-loading helpers can exploit this vulnerability to make a strict-mode application send
A vulnerability in `nltk.downloader` in nltk/nltk versions <= 3.9.4 allows for cross-package resource and model poisoning. The downloader extracts package archives into shared namespaces such as `corpora/` and `taggers/` instead of package-isolated roots, and validates package integrity only after the archive has been written and extracted. This design flaw enables one package to overwrite another package's trusted resources within the same namespace, making the changes immediately active through ordinary NLTK APIs. This issue persists across fresh interpreter restarts and can affect downstream workflows, including machine learning pipelines
In nltk version 3.9.4, the `nltk.downloader.Downloader._download_package()` function writes downloaded package bytes to disk and may extract them before enforcing SHA-256 or MD5 checksum validation. This allows an attacker to tamper with the package response body for `info.url` through a compromised mirror, malicious proxy, or other source-substitution condition, leading to the installation of attacker-controlled package bytes. The vulnerability can result in malicious corpus or model content being trusted by downstream users or applications.
NLTK (Natural Language Toolkit) before version 3.9.3 contains an eval injection vulnerability in the nltk.collocations module that allows an attacker who controls command-line arguments to execute arbitrary Python code. When collocations.py is invoked directly, the __main__ block passes command-line arguments directly to eval() as suffixes of BigramAssocMeasures without allowlist validation or sanitization, enabling an attacker to supply a Python expression that escapes the intended attribute lookup and executes arbitrary code including OS commands via the os module.
In nltk/nltk versions 3.9.3 and earlier, five Stanford interface classes (StanfordPOSTagger, StanfordNERTagger, StanfordParser, StanfordDependencyParser, and StanfordNeuralDependencyParser) are vulnerable to untrusted JAR code execution. These classes accept user-controllable JAR paths and execute them via the `java()` function, which invokes `subprocess.Popen()` without integrity verification. This vulnerability is identical to CVE-2026-0848, which was fixed for StanfordSegmenter by adding SHA256 verification. However, the fix was not applied to these additional classes, leaving them susceptible to arbitrary code execution when loading untru
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