CVE-2026-115927 in Tornado
Summary
by MITRE • 10/26/2026
Tornado is a Python web framework and asynchronous networking library. Prior to 6.6.0, Tornado decodes HTTP/1.1 chunked transfer encoding by processing body data that has already arrived without yielding to the event loop. A remote unauthenticated peer can send millions of one-byte chunks to monopolize the process event loop and stall every other connection served by that process, while lowering max_body_size only limits the duration of each request. For handlers that do not use @stream_request_body, retaining each chunk as a separate object can consume roughly twenty times the received body size, and SimpleAsyncHTTPClient has the same event-loop behavior when reading a response from a malicious server. This issue is fixed in version 6.6.0.
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Analysis
by VulDB Data Team • 10/26/2026
Tornado is a widely used Python web framework designed for high-performance asynchronous networking applications. A critical vulnerability exists within versions prior to 6.6.0 regarding the handling of HTTP/1.1 chunked transfer encoding. The core technical flaw lies in how Tornado processes incoming body data during this specific encoding method. Instead of yielding control back to the event loop after receiving each chunk, the framework continues processing the data synchronously within a single iteration cycle. This design choice creates a significant opportunity for resource exhaustion attacks by remote unauthenticated actors who can exploit the lack of cooperative multitasking points in the request parsing logic.
The operational impact of this vulnerability is severe and multifaceted. An attacker can send millions of one-byte chunks to a Tornado server, effectively monopolizing the process event loop. Because Python's asyncio relies on explicit yields to switch between tasks, keeping the loop busy with continuous data processing prevents it from servicing other concurrent connections. This results in a denial-of-service condition where every other connection served by that specific worker process is stalled or delayed significantly. The vulnerability persists even if administrators attempt to mitigate it by lowering the max_body_size configuration parameter, as this setting only limits the total duration of each individual request rather than preventing the event loop from being held hostage during the parsing phase itself.
Beyond availability issues related to the event loop, there are substantial memory consumption risks associated with this flaw. For HTTP handlers that do not utilize the @stream_request_body decorator, Tornado retains each received chunk as a separate object in memory. This behavior leads to excessive memory allocation, consuming roughly twenty times the actual size of the received body data. In scenarios involving large volumes of small chunks, this can rapidly exhaust available system memory, leading to application crashes or instability. Additionally, the SimpleAsyncHTTPClient component exhibits similar event-loop blocking behavior when reading responses from malicious servers, extending the attack surface beyond just incoming server requests to include outgoing client interactions that may encounter untrusted upstream services.
This vulnerability is classified under CWE-400, which covers Uncontrolled Resource Consumption, specifically highlighting how improper handling of input can lead to resource exhaustion. From a threat modeling perspective aligned with MITRE ATT&CK techniques, this behavior facilitates Denial-of-Service (DoS) attacks through event loop starvation and potentially contributes to Remote Code Execution vectors if memory corruption occurs due to excessive allocation pressures in lower-level components or associated libraries. The issue is resolved in version 6.6.0 of Tornado, which implements fixes to ensure that the event loop yields appropriately during chunked transfer encoding processing. Organizations running affected versions should upgrade immediately and review their configurations for any reliance on unstreamed request body handling to mitigate both performance degradation and memory exhaustion risks.