urllib3 is an HTTP client library for Python. urllib3's streaming API is designed for the efficient handling of large HTTP responses by reading the content in chunks, rather than loading the entire response body into memory at once. urllib3 can perform decoding or decompression based on the HTTP `Content-Encoding` header (e.g., `gzip`, `deflate`, `br`, or `zstd`). When using the streaming API, the library decompresses only the necessary bytes, enabling partial content consumption. Starting in version 1.22 and prior to version 2.6.3, for HTTP redirect responses, the library would read the entire response body to drain the connection and decompress the content unnecessarily. This decompression occurred even before any read methods were called, and configured read limits did not restrict the amount of decompressed data. As a result, there was no safeguard against decompression bombs. A malicious server could exploit this to trigger excessive resource consumption on the client. Applications and libraries are affected when they stream content from untrusted sources by setting `preload_content=False` when they do not disable redirects. Users should upgrade to at least urllib3 v2.6.3, in which the library does not decode content of redirect responses when `preload_content=False`. If upgrading is not immediately possible, disable redirects by setting `redirect=False` for requests to untrusted source.
This vulnerability carries a HIGH severity rating with a CVSS v3.1 score of 7.5, indicating it can be exploited remotely over the network with relatively low complexity without requiring user interaction and does not require pre-existing privileges . The vulnerability impacts and availability (service disruption) for affected systems. Impacting 1 product from python organizations running these solutions should prioritize assessment and patching.
Reported in 2026, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.
2026-01-07T22:15:44.040
2026-01-23T09:15:47.823
Modified
CVSSv3.1: 7.5 (HIGH)
SecUtils normalizes and enriches National Vulnerability Database (NVD) records by standardizing vendor and product identifiers, aggregating vulnerability metadata from both NVD and MITRE sources, and providing structured context for security teams. For python's affected products, we extract Common Platform Enumeration (CPE) data, Common Weakness Enumeration (CWE) classifications, CVSS severity metrics, and reference data to enable rapid vulnerability prioritization and asset correlation. This record contains no exploit code, proof-of-concept instructions, or attack methodologies—only defensive intelligence necessary for patch management, risk assessment, and security operations.