The vulnerability is present in PunktSentenceTokenizer
, sent_tokenize
and word_tokenize
. Any users of this class, or these two functions, are vulnerable to a Regular Expression Denial of Service (ReDoS) attack.
In short, a specifically crafted long input to any of these vulnerable functions will cause them to take a significant amount of execution time. The effect of this vulnerability is noticeable with the following example:
from nltk.tokenize import word_tokenize
n = 8
for length in [10**i for i in range(2, n)]:
# Prepare a malicious input
text = "a" * length
start_t = time.time()
# Call `word_tokenize` and naively measure the execution time
word_tokenize(text)
print(f"A length of {length:<{n}} takes {time.time() - start_t:.4f}s")
Which gave the following output during testing:
A length of 100 takes 0.0060s
A length of 1000 takes 0.0060s
A length of 10000 takes 0.6320s
A length of 100000 takes 56.3322s
...
I canceled the execution of the program after running it for several hours.
If your program relies on any of the vulnerable functions for tokenizing unpredictable user input, then we would strongly recommend upgrading to a version of NLTK without the vulnerability, or applying the workaround described below.
The problem has been patched in NLTK 3.6.6. After the fix, running the above program gives the following result:
A length of 100 takes 0.0070s
A length of 1000 takes 0.0010s
A length of 10000 takes 0.0060s
A length of 100000 takes 0.0400s
A length of 1000000 takes 0.3520s
A length of 10000000 takes 3.4641s
This output shows a linear relationship in execution time versus input length, which is desirable for regular expressions. We recommend updating to NLTK 3.6.6+ if possible.
The execution time of the vulnerable functions is exponential to the length of a malicious input. With other words, the execution time can be bounded by limiting the maximum length of an input to any of the vulnerable functions. Our recommendation is to implement such a limit.
If you have any questions or comments about this advisory: * Open an issue in github.com/nltk/nltk * Email us at nltk.team@gmail.com