CVE-2025-69230: Aiohttp Resource Exhaustion via Cookie Logging Storm

CVE-2025-69230 is a resource exhaustion vulnerability affecting aiohttp, a ubiquitous asynchronous HTTP client/server framework for Python. The vulnerability manifests in the HTTP cookie parsing logic, specifically within the _cookie_helpers.py module. It is classified under CWE-779 (Uncontrolled Log Activities), often referred to as a "Logging Storm."

In affected versions, the application does not limit the number of log entries generated when processing invalid inputs in the Cookie HTTP header. When an HTTP request includes cookies with invalid characters (such as commas or control characters) in their names, the parser triggers a WARNING level log event for each invalid cookie found.

Because the Cookie header can hold a significant amount of data (often up to 8KB or more depending on server configuration), an attacker can pack thousands of short, invalid cookie definitions into a single request. This forces the application to execute thousands of logging calls for a single incoming connection, disproportionately consuming CPU cycles for string formatting and I/O bandwidth for writing logs.

The root cause lies in the iterative validation logic within the parse_cookie_header function. The function is designed to split the Cookie header string by semicolons and validate each key-value pair. The validation checks the cookie name against a regular expression, _COOKIE_NAME_RE, to ensure it contains only legal characters.

Prior to version 3.13.3, the code handled validation failures by immediately emitting a log message within the parsing loop. The logic proceeded as follows:

1. Split the header string into potential cookie pairs.
2. Iterate over every pair.
3. If the name fails the regex check, call internal_logger.warning().
4. Continue to the next pair.

This implementation creates an O(n) relationship between the number of malformed cookies provided by the client and the number of log write operations performed by the server. Since logging operations often involve synchronous I/O (writing to a file or standard error) or expensive network calls (sending to a log collector), this linear scaling allows an attacker to amplify the cost of processing a single HTTP request significantly.

The vulnerability is evident when comparing the loop implementation in aiohttp before and after the patch. The remediation strategy involves decoupling the identification of invalid cookies from the logging action.

Vulnerable Code (Simplified):

# Inside parse_cookie_header()
for key, value in parsed_cookies:
    if not _COOKIE_NAME_RE.match(key):
        # VULNERABILITY: Immediate logging inside the loop
        # A separate log entry is created for EVERY invalid key
        internal_logger.warning("Can not load cookie: Illegal cookie name %r", key)

Patched Code (Commit 64629a0):

# Inside parse_cookie_header()
invalid_names = [] # buffer for invalid keys
 
for key, value in parsed_cookies:
    if not _COOKIE_NAME_RE.match(key):
        # FIX: Collect the invalid key, do not log yet
        invalid_names.append(key)
 
# ... loop finishes ...
 
# FIX: Single log entry for the entire request
if invalid_names:
    # FIX: Downgrade from WARNING to DEBUG to reduce noise
    internal_logger.debug("Cannot load cookie. Illegal cookie names: %r", invalid_names)

The patch introduces two critical changes:

1. Aggregation: Errors are collected in a list (invalid_names) and logged in a single batch operation after parsing completes. This changes the logging complexity from O(n) to O(1) per request.
2. Severity Reduction: The log level is changed from WARNING to DEBUG. In most production environments, DEBUG logs are disabled, meaning the attack payload will trigger zero log output.

Exploitation of CVE-2025-69230 is trivial and requires no authentication. The attacker constructs a standard HTTP request but populates the Cookie header with a repetitive pattern of invalid keys. Specifically, characters like the comma (,) are typically disallowed in cookie names but valid as separators in some contexts, triggering the validation failure.

Proof of Concept:

import requests
 
# Target URL
target = "http://localhost:8080/"
 
# Construct a payload with 3,000 malformed cookies
# Each entry "bad,cookieN=1" triggers the regex failure due to the comma
payload = "; ".join(f"bad,cookie{i}=1" for i in range(3000))
 
headers = {
    "Cookie": payload
}
 
# Sending this request causes the server to write 3,000 log lines
requests.get(target, headers=headers)

When the server processes this request, it attempts to write 3,000 distinct lines to its log output. If an attacker sends these requests concurrently (e.g., 100 requests per second), the server attempts to write 300,000 log lines per second. This I/O pressure can saturate the disk write buffer, blocking the main event loop in synchronous logging configurations, or flood remote log aggregators (e.g., Splunk, ELK), leading to increased infrastructure costs and dropped alerts.

The primary impact of this vulnerability is Denial of Service (DoS) targeting the application's resources and observability infrastructure. While the CVSS v3.1 score is 5.3 (Medium), the operational impact can be severe depending on the logging configuration.

• Resource Exhaustion: The formatting of log strings and the subsequent I/O operations consume CPU and disk bandwidth. If logs are written to a slow disk, the application performance degrades significantly.
• Log Flooding: Security teams rely on logs for anomaly detection. A flood of false positives makes it difficult to detect genuine attacks occurring simultaneously. Furthermore, cloud-based logging services often charge by ingestion volume; this attack can artificially inflate operational costs.
• Disk Space Exhaustion: In environments without aggressive log rotation, the rapid generation of text logs can consume available disk space, potentially causing the OS or application to crash.

It is important to note that this vulnerability does not allow for Remote Code Execution (RCE) or unauthorized data access. The C:L (Confidentiality Low) vector in some reports likely refers to the theoretical possibility of side-channel leakages via logs, but the primary risk is Availability.

The vulnerability is addressed in aiohttp version 3.13.3. All users of the library should upgrade immediately.

Upgrade Instructions:

pip install aiohttp>=3.13.3

Mitigation Strategies:

If an immediate upgrade is not feasible, the following mitigations can reduce the impact:

1. Log Level Configuration: Configure the application's logger to suppress WARNING level logs from the aiohttp.server or aiohttp.internal loggers, raising the threshold to ERROR.
2. WAF Filtering: Implement a Web Application Firewall (WAF) rule to inspect the Cookie header. Block requests where the number of semicolons exceeds a reasonable threshold (e.g., 50) or where cookie names contain known illegal characters like commas.
3. Reverse Proxy Sanitization: Use a reverse proxy (e.g., Nginx or HAProxy) in front of the python application to parse and drop invalid cookies before they reach the aiohttp backend.