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pyLoad: Rate-Limit Bypass and Audit-Log Spoofing via Trusted Client-Controlled `X-Forwarded-For` Header

Moderate severity GitHub Reviewed Published Oct 8, 2026 in pyload/pyload • Updated Oct 9, 2026

Package

pip pyload-ng (pip)

Affected versions

= 0.5.0b3.dev101

Patched versions

None

Description

Summary

pyLoad determines the "client IP" used for its rate-limiting decorator and for its
security/audit logging by reading the client-supplied X-Forwarded-For (XFF) HTTP header and
taking the leftmost value. No trusted-proxy configuration exists (the Cheroot WSGI server faces
clients directly, and there is no ProxyFix middleware). Because any client can freely set this
header, an attacker can (1) completely bypass rate limiting by rotating the header on each request,
and (2) forge the source IP recorded in security logs for login and API-key authentication
failures, defeating IP-based blocking (e.g. fail2ban) and poisoning attribution.

Severity

Medium — CVSS 3.1: AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:L (~5.3)

  • CWE-807: Reliance on Untrusted Inputs in a Security Decision
  • CWE-290: Authentication Bypass by Spoofing
  • CWE-348: Use of Less Trusted Source

Affected Version

pyLoad 0.5.0b3 (built from the develop branch). Confirmed still present and exploitable in the
later pyload-develop-2 snapshot (the affected files are byte-identical between the two; the
develop-2 changes only touched the unrelated API-key cache).

Affected Component

Web UI request handling — client-IP derivation used by rate limiting and security logging.

  • src/pyload/webui/app/helpers.py:446 — inside the rate_limit() decorator; derives the per-IP
    bucket key.
  • src/pyload/webui/app/helpers.py:357 — API-key authentication success/failure logging.
  • src/pyload/webui/app/blueprints/app_blueprint.py:81 — web login success/failure logging.
  • src/pyload/webui/webserver_thread.py — Cheroot serves the Flask app directly; no reverse-proxy
    trust boundary or ProxyFix.
  • Consumer: src/pyload/webui/app/blueprints/api_blueprint.py:25 applies
    @rate_limit(count=100, period=60) to the /api/<func> RPC endpoint.

Description:

All three locations derive the client IP with the identical expression:

client_ip = flask.request.headers.get("X-Forwarded-For", "").split(",")[0].strip() or flask.request.remote_addr

X-Forwarded-For is an HTTP request header fully controlled by the client. The code takes
split(",")[0] — the leftmost token — which is always the value supplied by the original client
(a proxy appends its own value to the right). Only when the header is entirely absent does the code
fall back to request.remote_addr (the real TCP peer). There is no configured trusted-proxy count,
and pyLoad's server (Cheroot) terminates client connections directly, so remote_addr is the true
peer and the XFF value is untrusted attacker input.

Two security decisions are made on this untrusted value:

  1. Rate limiting (rate_limit()): the decorator keeps request_history[client_ip] and enforces
    count requests per period per client_ip. Since client_ip is attacker-controlled, sending
    a distinct value each request creates a distinct bucket, so the counter never accumulates and
    the limit never triggers.
  2. Audit logging: login-failure and API-auth-failure log lines embed [CLIENT: {client_ip}] using
    the same spoofable value, so the recorded source address is chosen by the attacker.

Notably, the codebase is internally inconsistent: is_loopback_request() (helpers.py:288) treats
the presence of X-Forwarded-For / X-Real-IP / Forwarded as a reason to distrust an
apparent loopback source, while the rate-limit and logging paths naively trust the same header —
indicating the trust here is unintended.

Proof of Concept

The rate_limit() decorator's actual source (bytes read from
pyload-develop-2/src/pyload/webui/app/helpers.py, lines 416–514) was executed under a real Flask
test client. Rate limit set to 5/60s for brevity (identical logic to the production 100/min):

import ast, math, time
from functools import wraps
import flask

HELPERS = ".../pyload-develop-2/src/pyload/webui/app/helpers.py"
src = open(HELPERS).read()
node = next(n for n in ast.parse(src).body
            if isinstance(n, ast.FunctionDef) and n.name == "rate_limit")
ns = {"flask": flask, "time": time, "math": math, "wraps": wraps}
exec(compile(ast.get_source_segment(src, node), HELPERS, "exec"), ns)
rate_limit = ns["rate_limit"]

def build_app():
    app = flask.Flask(__name__)
    @app.route("/api/ping")
    @rate_limit(count=5, period=60)
    def ping():
        return flask.json.jsonify({"ok": True})
    return app

def run(hdr):
    app = build_app()
    with app.test_client() as c:
        return [c.get("/api/ping", headers=hdr(i)).status_code for i in range(8)]

print("FIXED   XFF:", run(lambda i: {"X-Forwarded-For": "203.0.113.9"}))
print("ROTATE  XFF:", run(lambda i: {"X-Forwarded-For": f"10.0.0.{i}"}))

Output:

FIXED   XFF: [200, 200, 200, 200, 200, 429, 429, 429]     # limit enforced
ROTATE  XFF: [200, 200, 200, 200, 200, 200, 200, 200]     # 0x 429 -> limit bypassed
# server log during FIXED run:
#   WARNING in helpers: Rate limit exceeded for IP 203.0.113.9: 5 requests in 60 (limit: 5/60)

Derivation check (XFF present, real peer is loopback):

# request with header X-Forwarded-For: 8.8.8.8 and REMOTE_ADDR 127.0.0.1
client_ip = request.headers.get("X-Forwarded-For","").split(",")[0].strip() or request.remote_addr
# -> client_ip == "8.8.8.8"   (the spoofed value wins over the real remote_addr)

Equivalent against a live instance:

# Rate limit never trips regardless of volume:
for i in $(seq 1 500); do
  curl -s -o /dev/null -H "X-API-Key: pl_1<valid-key>" \
       -H "X-Forwarded-For: 10.0.0.$((RANDOM%255))" \
       http://127.0.0.1:8000/api/get_server_version
done   # no HTTP 429 is ever returned

# Forged source IP in the audit log:
curl -s -o /dev/null -H "X-Forwarded-For: 8.8.8.8" \
     --data 'username=admin&password=wrong' http://127.0.0.1:8000/login
# server log: Login failed for user 'admin' using Web Client [CLIENT: 8.8.8.8]

Steps To Reproduce

  1. Start a pyLoad instance directly exposed (no reverse proxy), as in a default deployment.
  2. Obtain any valid API key (or use the login endpoint) so requests reach the rate-limited /
    logged code paths.
  3. Send more than the configured limit of requests to a @rate_limit-protected endpoint (e.g.
    /api/get_server_version) while setting a different X-Forwarded-For value on each request.
    Observe that no 429 Too Many Requests is ever returned.
  4. Control: repeat step 3 with a fixed X-Forwarded-For value; observe 429 after the limit,
    proving the bucket is keyed on the header value.
  5. Send a failed login (or failed API-key request) with X-Forwarded-For: 8.8.8.8 and inspect the
    server log; the failure is attributed to 8.8.8.8 rather than the real client address.

Impact

  • Rate limiting on the authenticated API (/api/<func>, 100/min) and any other
    @rate_limit-protected endpoint provides no protection against a single attacker, who can issue
    unlimited requests — enabling resource abuse and unthrottled brute-forcing of anything reachable
    through those endpoints.
  • Security/audit logs cannot be trusted for source attribution. An attacker can stamp every
    malicious request with an arbitrary or innocent third-party IP, evading IP-based blocklists /
    fail2ban and misdirecting incident response.
  • Amplifies other authentication weaknesses (e.g. the absence of throttling on /login), since
    even the throttling that does exist elsewhere is defeated and the attacker's IP is unlogged.

Root Causes

  1. A client-controlled HTTP header (X-Forwarded-For) is used directly in security decisions
    (rate-limit bucketing and audit attribution) without any trusted-proxy validation (CWE-807).
  2. The leftmost XFF token is selected, which is always the value supplied by the original client;
    this is spoofable both in direct-exposure and behind-a-proxy deployments (CWE-348).
  3. No ProxyFix / trusted-proxy-hop configuration exists, and the server terminates client
    connections directly, so there is no basis for trusting forwarded headers at all.
  4. Inconsistent handling across the codebase (loopback checks distrust these headers while
    rate-limit/logging trust them) indicates the trust is accidental rather than designed.

Suggested Fix

  1. By default, use request.remote_addr (the real TCP peer) for rate limiting and logging; do not
    trust X-Forwarded-For unless a proxy is explicitly configured.
  2. Add an explicit, operator-configured trusted-proxy count (default 0 = trust none). When set,
    apply Werkzeug's ProxyFix(app.wsgi_app, x_for=N) and read the correct hop (the value inserted
    by the trusted proxy — typically the Nth-from-right token — not the leftmost client-supplied
    token).
  3. Derive the trusted client IP once in a single helper and use it consistently for rate limiting,
    logging, and the loopback checks, removing the duplicated inline expressions.
  4. Consider defense-in-depth: cap the number of distinct rate-limit buckets and/or additionally
    rate-limit on remote_addr so a spoofed header cannot create unbounded buckets.

Invariant to restore: security decisions and audit attribution must be based on a connection-level
or trusted-proxy-validated address, never on a raw client-supplied header.

References

@GammaC0de GammaC0de published to pyload/pyload Oct 8, 2026
Published to the GitHub Advisory Database Oct 9, 2026
Reviewed Oct 9, 2026
Last updated Oct 9, 2026

Severity

Moderate

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 None
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity Low
Availability None
Subsequent System Impact Metrics
Confidentiality None
Integrity None
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:N/UI:N/VC:N/VI:L/VA:N/SC:N/SI:N/SA:N

EPSS score

Weaknesses

Authentication Bypass by Spoofing

This attack-focused weakness is caused by incorrectly implemented authentication schemes that are subject to spoofing attacks. Learn more on MITRE.

CVE ID

No known CVE

GHSA ID

GHSA-9q47-3cm2-2rp8

Source code

Credits

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