[jira] [Commented] (THRIFT-3175) fastbinary.c python deserialize can cause huge allocations from garbage

["Roger Meier (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/THRIFT-3175?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14567954#comment-14567954 ] 

Roger Meier commented on THRIFT-3175:
-------------------------------------

Thanks for catching this! Could you provide a patch?

> fastbinary.c python deserialize can cause huge allocations from garbage
> -----------------------------------------------------------------------
>
>                 Key: THRIFT-3175
>                 URL: https://issues.apache.org/jira/browse/THRIFT-3175
>             Project: Thrift
>          Issue Type: Bug
>          Components: Python - Library
>            Reporter: Dvir Volk
>
> In the fastbinary python deserializer, allocating a list is done like so:
> {code}
>     len = readI32(input);
>     if (!check_ssize_t_32(len)) {
>       return NULL;
>     }
>     ret = PyList_New(len);
> {code}
> The only validation of len is that it's under INT_MAX. I've encountered a situation where upon receiving garbage input, and having len be read as something like 1 billion, the library treated this as a valid input, allocated gigs of RAM,  and caused a server to crash. 
> The quick fix I made was to limit list sizes to a sane value of a few thousands that more than suits my personal needs. 
> But IMO this should be dealt with properly. One way that comes to mind is not pre-allocating the entire list in advance in case it's really big, and resizing it in smaller steps while reading the input. 



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