CVE-2024-0243
Crawler downloads external links despite prevention settings.
- CVSS 8.1
- CWE-918
- Input Validation and Sanitization
- Remote
With the following crawler configuration: “`python from bs4 import BeautifulSoup as Soup url = "https://example.com" loader = RecursiveUrlLoader( url=url, max_depth=2, extractor=lambda x: Soup(x, "html.parser").text ) docs = loader.load() “` An attacker in control of the contents of `https://example.com` could place a malicious HTML file in there with links like "https://example.completely.different/my_file.html" and the crawler would proceed to download that file as well even though `prevent_outside=True`. https://github.com/langchain-ai/langchain/blob/bf0b3cc0b5ade1fb95a5b1b6fa260e99064c2e22/libs/community/langchain_community/document_loaders/recursive_url_loader.py#L51-L51 Resolved in https://github.com/langchain-ai/langchain/pull/15559
- CWE
- CWE-918
- CVSS base score
- 8.1
- Published
- 2024-02-26
- OWASP
- A01 Broken Access Control
- Orthogonal defect classification
- Algorithm
- Code defect classification
- Incorrect Algorithm
- Category
- Input Validation and Sanitization
- Subcategory
- Server-Side Request Forgery (SSRF)
- Accessibility scope
- Remote
- Impact
- Unauthorized Access
- Affected component
- Langchain
- Fixed by upgrading
- Yes
Solution
Upgrade to version 0.0.195 or later, where the vulnerability has been resolved.
Vulnerable code sample
from bs4 import BeautifulSoup as Soup
import requests
class RecursiveUrlLoader:
def __init__(self, url, max_depth, extractor, prevent_outside=True):
self.url = url
self.max_depth = max_depth
self.extractor = extractor
self.prevent_outside = prevent_outside
self.visited_urls = set()
def load(self):
return self._load_recursive(self.url, 0)
def _load_recursive(self, url, depth):
if depth > self.max_depth or url in self.visited_urls:
return []
self.visited_urls.add(url)
response = requests.get(url)
content = self.extractor(response.text)
links = self.extract_links(response.text)
documents = [content]
for link in links:
documents.extend(self._load_recursive(link, depth + 1))
return documents
def extract_links(self, html):
soup = Soup(html, "html.parser")
return [a['href'] for a in soup.find_all('a', href=True)]
url = "https://example.com"
loader = RecursiveUrlLoader(
url=url, max_depth=2, extractor=lambda x: Soup(x, "html.parser").text
)
docs = loader.load()Patched code sample
from bs4 import BeautifulSoup as Soup
import requests
class RecursiveUrlLoader:
def __init__(self, url, max_depth, extractor, prevent_outside=True):
self.url = url
self.max_depth = max_depth
self.extractor = extractor
self.prevent_outside = prevent_outside
self.visited_urls = set()
def check_url(self, url):
return url.startswith(self.url)
def load(self):
return self._load_recursive(self.url, 0)
def _load_recursive(self, url, depth):
if depth > self.max_depth or url in self.visited_urls:
return []
self.visited_urls.add(url)
response = requests.get(url)
content = self.extractor(response.text)
links = self.extract_links(response.text)
documents = [content]
for link in links:
if self.prevent_outside and not self.check_url(link):
continue
documents.extend(self._load_recursive(link, depth + 1))
return documents
def extract_links(self, html):
soup = Soup(html, "html.parser")
return [a['href'] for a in soup.find_all('a', href=True)]
url = "https://example.com"
loader = RecursiveUrlLoader(
url=url, max_depth=2, extractor=lambda x: Soup(x, "html.parser").text
)
docs = loader.load()Payload
<a href="https://example.completely.different/my_file.html">Malicious Link</a>
Cite this entry
@misc{vaitp:cve20240243,
title = {{Crawler downloads external links despite prevention settings.}},
author = {Bogaerts, Fr\'ed\'eric and Ivaki, Naghmeh and Fonseca, Jos\'e},
year = {2024},
note = {VAITP Python Vulnerability Dataset, entry CVE-2024-0243},
howpublished = {\url{https://netpack.pt/vaitp/vulnerability/CVE-2024-0243/}}
}
Introducing the "VAITP dataset": a specialized repository of Python vulnerabilities and patches, meticulously compiled for the use of the security research community. As Python's prominence grows, understanding and addressing potential security vulnerabilities become crucial. Crafted by and for the cybersecurity community, this dataset offers a valuable resource for researchers, analysts, and developers to analyze and mitigate the security risks associated with Python. Through the comprehensive exploration of vulnerabilities and corresponding patches, the VAITP dataset fosters a safer and more resilient Python ecosystem, encouraging collaborative advancements in programming security.
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